Livermore school ratings: Public Elementary Schools in Livermore, CA

Опубликовано: January 9, 2023 в 5:33 am

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Категории: Miscellaneous

Public Elementary Schools in Livermore, CA

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1-20 of 20 results

  1. #157 Best Public Elementary Schools in California

    #157 Best Public Elementary Schools in California.

    Bella Vista Elementary School

    San Ramon Valley Unified School District, CA,

    K-5,

    1 Niche users give it an average review of 5 stars.

    Featured Review: Sophomore says I went there for 5th grade. I love the smell of the new buildings and had an awesome time there. The teachers are nice and caring. This is a glitch. It is in San Ramon not in Solano County. There are five other schools in the district that are glitched outside the district boundaries. Please fix them..

    Read 1 reviews.

    Overall Niche Grade: A,

    Students: 564,

    Student-Teacher Ratio: 19 to 1,

  2. #184 Best Public Elementary Schools in California

    #184 Best Public Elementary Schools in California.

    Cottonwood Creek

    Blue checkmark.

    Dublin Unified School District, CA,

    K-8,

    1 Niche users give it an average review of 5 stars.

    Featured Review: Parent says My daughter loves going to school every morning because of the wonderful teachers and feeling of belonging. The school has great infrastructure and teachers. They really help the kids making….

    Read 1 reviews.

    Overall Niche Grade: A,

    Students: 1,017,

    Student-Teacher Ratio: 22 to 1,

  3. #491 Best Public Elementary Schools in California

    #491 Best Public Elementary Schools in California.

    Hansen Elementary

    Blue checkmark.

    Lammersville Joint Unified School District, CA,

    K-8,

    Overall Niche Grade: A,

    Students: 768,

    Student-Teacher Ratio: 26 to 1,

  5. #758 Best Public Elementary Schools in California

    #758 Best Public Elementary Schools in California.

    Emma C. Smith Elementary School

    Blue checkmark.

    Livermore Valley Joint Unified School District, CA,

    K-5,

    Overall Niche Grade: A,

    Students: 666,

    Student-Teacher Ratio: 26 to 1,

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  6. #829 Best Public Elementary Schools in California

    #829 Best Public Elementary Schools in California.

    Sunset Elementary School

    Blue checkmark.

    Livermore Valley Joint Unified School District, CA,

    K-5,

    Overall Niche Grade: A minus,

    Students: 744,

    Student-Teacher Ratio: 26 to 1,

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  7. Altamont Elementary School

    Blue checkmark.

    Lammersville Joint Unified School District, CA,

    K-8,

    1 Niche users give it an average review of 4 stars.

    Featured Review: Middle School Student says Altamint Elementary School is a rich and diverse school where any student can learn and flourish. The middle school teachers are excellent, but I can’t say the same for all the lower grade teachers..

    Read 1 reviews.

    Overall Niche Grade: A minus,

    Students: 683,

    Student-Teacher Ratio: 24 to 1,

  8. Altamont Creek Elementary School

    Blue checkmark.

    Livermore Valley Joint Unified School District, CA,

    K-5,

    Overall Niche Grade: A minus,

    Students: 586,

    Student-Teacher Ratio: 23 to 1,

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  9. Lawrence Elementary

    Blue checkmark.

    Livermore Valley Joint Unified School District, CA,

    K-5,

    Overall Niche Grade: A minus,

    Students: 362,

    Student-Teacher Ratio: 23 to 1,

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  10. Rancho Las Positas Elementary School

    Blue checkmark.

    Livermore Valley Joint Unified School District, CA,

    K-5,

    Overall Niche Grade: A minus,

    Students: 594,

    Student-Teacher Ratio: 23 to 1,

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  12. Jackson Avenue Elementary School

    Blue checkmark.

    Livermore Valley Joint Unified School District, CA,

    K-5,

    Overall Niche Grade: A minus,

    Students: 486,

    Student-Teacher Ratio: 22 to 1,

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  13. Arroyo Seco Elementary School

    Blue checkmark.

    Livermore Valley Joint Unified School District, CA,

    K-5,

    1 Niche users give it an average review of 5 stars.

    Read 1 reviews.

    Overall Niche Grade: B+,

    Students: 636,

    Student-Teacher Ratio: 23 to 1,

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  14. Joe Michell

    Blue checkmark.

    Livermore Valley Joint Unified School District, CA,

    K-8,

    Overall Niche Grade: B+,

    Students: 778,

    Student-Teacher Ratio: 23 to 1,

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  15. Vineyard Alternative School

    Blue checkmark.

    Livermore Valley Joint Unified School District, CA,

    1-12,

    2 Niche users give it an average review of 5 stars.

    Featured Review: Senior says Not only is the staff incredibly welcoming and kind, they are also extremely accommodating. Teachers and staff are always there to help with anything whether it’s rush sending transcripts, adding….

    Read 2 reviews.

    Overall Niche Grade: B+,

    Students: 94,

    Student-Teacher Ratio: 8 to 1,

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  16. Leo R. Croce Elementary School

    Blue checkmark.

    Livermore Valley Joint Unified School District, CA,

    K-5,

    1 Niche users give it an average review of 4 stars.

    Featured Review: Alum says Very good school! Teachers care about the students education and help provide a safe and fun environment. They provide lots of programs for kids to help them succeed..

    Read 1 reviews.

    Overall Niche Grade: B+,

    Students: 556,

    Student-Teacher Ratio: 24 to 1,

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  17. Vista Oaks Charter School

    Public School,

    LODI, CA,

    K-12,

    5 Niche users give it an average review of 4. 2 stars.

    Featured Review: Parent says Homeschooling through Vista Oaks allows students to go at their own pace. It allows students to take college classes at the local community college while in highschool. Great classes are also….

    Read 5 reviews.

    Overall Niche Grade: B,

    Students: 874,

    Student-Teacher Ratio: 27 to 1,

  18. Nextgeneration Steam Academy

    Public School,

    LATHROP, CA,

    K-8,

    Overall Niche Grade: B,

    Students: 637,

    Student-Teacher Ratio: 24 to 1,

  19. Junction K-8

    Blue checkmark.

    Livermore Valley Joint Unified School District, CA,

    K-8,

    1 Niche users give it an average review of 2 stars.

    Read 1 reviews.

    Overall Niche Grade: B,

    Students: 927,

    Student-Teacher Ratio: 22 to 1,

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  20. Marylin Avenue Elementary School

    Blue checkmark.

    Livermore Valley Joint Unified School District, CA,

    K-5,

    Overall Niche Grade: B minus,

    Students: 362,

    Student-Teacher Ratio: 20 to 1,

  22. Central County Special Education Programs

    Contra Costa County Office of Education, CA,

    K-12,

    Students: 22,

    Student-Teacher Ratio: 6 to 1,

  24. View nearby homes Virtual tour

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    Livermore Valley Joint Unified School District – California

    Report Card

    1. Academics

      grade A minus

    2. Diversity

      grade A minus

    3. Teachers

      grade B+

    4. College Prep

      grade A+

    5. Clubs & Activities

      grade C

    6. Administration

      grade B

    7. Sports

      grade B

    8. Food

      grade C

    9. Resources & Facilities

      grade C minus

    editorial

    Livermore Valley Joint Unified School District is a highly rated, public school district located in LIVERMORE, CA. It has 13,305 students in grades K-12 with a student-teacher ratio of 23 to 1. According to state test scores, 50% of students are at least proficient in math and 63% in reading.

    About

    livermoreschools.org

    (925) 606-3200

    685 E. JACK LONDON BLVD.
    LIVERMORE, CA 94551

    Livermore Valley Joint Unified School District Rankings

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    Academics

    Percent Proficient – Reading

    This is the percentage of students that scored at or above proficiency levels on their state reading/language arts assessment test. Because states implement these tests differently, use caution when comparing this data to data from another state.

    63%

    Percent Proficient – Math

    This is the percentage of students that scored at or above proficiency levels on their state math assessment test. Because states implement these tests differently, use caution when comparing this data to data from another state.

    50%

    Average Graduation Rate

    This is the percentage of 12th graders who graduated. Because states calculate graduation rates differently, use caution when comparing this data to data from another state.

    95%

    Average SAT

    Average SAT composite score out of 1600, as reported by Niche users from this school.

    1250

    786 responses

    Average ACT

    Average ACT composite score out of 36, as reported by Niche users from this school.

    28

    267 responses

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    Students

    Diversity

    grade A minus

    Based on racial and economic diversity and survey responses on school culture and diversity from students and parents.

    Students

    13,305

    Free or Reduced Lunch

    This is the percentage of students who are eligible to receive free or reduced price lunch. Not all eligible students exercise this option, especially at non-traditional schools like online schools.

    23.2%

    Teachers

    Student-Teacher Ratio

    Student-Teacher Ratio may not be indicative of class size. It is calculated using the reported number of students and full-time equivalent teachers.

    23:1

    National

    17:1

    Average Teacher Salary

    $82,338

    Teachers in First/Second Year

    7.1%

    Finances

    Expenses Per Student

    $0 

    / student

    National

    $12,239

    • Instruction

      61%

    • Support Services

      37%

    • Other

      2%

    1. Cost of Living

      grade D

    2. Good for Families

      grade B+

    3. Housing

      grade C minus

    Median Household Income

    $131,664

    National

    $64,994

    Median Rent

    $2,164

    National

    $1,096

    Median Home Value

    $806,100

    National

    $229,800

    Livermore Valley Joint Unified School District Reviews

    Rating 3. 9 out of 5  39 reviews

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    I have really enjoyed Las Positas’s design program. I am finishing my second semester, and ready to enroll in my third on November 20th. I like the fact that only two teachers teach the required classes for graduation, I have built a strong relationship with these teachers and appreciate their techniques. My first semester I had taken one of the hardest classes, hand drafting. I did not receive a perfect grade, but felt that I learned more in that class than any class ever before. I did not give this school 5 stars because I have unfortunately taken a few classes where I felt that the teacher did the absolute bare minimum. One teacher for example would take exactly 2 weeks to grade every single assignment. This was incredibly unfortunate when the next weeks assignment was based off of the last. She took 4 days minimum to respond to any email.

    Start Your Review of Livermore Valley Joint Unified School District

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    Education wise Livermore school district is great, there are all types of opportunities offered.,All teachers that I have encountered throughout the years in Livermore have been very persistent in motivating their students rot do their best.However in the past years Livermore has not been very diverse as it is becoming and as an African American girl in a predominately white town I have face lots of prejudice and racism.

    Teachers were great, issue was how the board handled a lot of serious issues (particularly funding for LHS). Overall, day-to-day it was a very positive environment and people working there were very receptive to working with independent organizations to learn about climate change and try to get people involved.

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    National features of the path to open science / / Nezavisimaya gazeta

    Ethical norms of research will be much more difficult to change than organizational and financial schemes

    Tags: scientists, research, open science, scientific community, scientific publications

    Researchers from the Livermore National Laboratory (USA) are working inside the laser fusion facility. The goal is to focus inside the giant sphere 192 laser beams on a tiny hydrogen fuel pellet to start the reaction. Reuters photo

    The term “open science” is commonly associated with publications in open access journals and open platforms. However, this concept is constantly being supplemented with new elements, and the pandemic, due to which online modes of operation have spread, has accelerated its development.

    In 2021, UNESCO issued recommendations for the development of “open science”. They note that it has become “an inclusive construct that brings together various movements and practices aimed at making multilingual scientific knowledge open, accessible and reusable for all, to increase collaboration and information exchange for the benefit of science and society, and to open processes for creating, evaluating and transferring scientific knowledge to society outside the traditional scientific community” (UNESCO Recommendation on Open Science, 2021). UNESCO experts include in this concept open access to scientific publications, research data, software, codes and hardware; open scientific infrastructures; open engagement of public actors and open dialogue with other knowledge systems.

    “Fair” replacement for journal articles

    Thus, the transition to open science is a major change, as it contributes to the emergence of new organizational forms, technical means, financial models, and also affects the ethical standards in science.

    The countries of Europe and Asia are most energetically moving towards open science, including by adopting state plans for its development. So, in France, the government has already released the Second National Plan for Open Science for 2021–2024 (Second French Plan for Open Science. Generalising open science in France. 2021–2024), according to which the budget for it is tripled. With these funds, it is planned to create a national platform for scientific data and encourage their reuse; promote an open source policy; develop open science skills.

    The direction of open science, which began to develop one of the first, is open access to publications in scientific journals. In this case, the authors themselves pay for the publication of articles, usually at the expense of grants or scientific organizations in which they work. That is, the authors themselves become the source of funding for scientific publications instead of subscribers. The effectiveness of such a “transition” has not yet been proven.

    However, there is a growing number of public research foundations demanding that results be published in open access journals. And more and more journals, especially those with high scientometric indicators, are moving to a new publication policy.

    From the point of view of scientometrics, open access is beneficial to all participants in the process: studies confirm that open access articles are more often cited and have a significantly higher number of downloads. According to Springer Nature, the leaders in the transition to open access are medicine (this topic accounts for almost 45% of all open access articles) and life sciences.

    It is curious that, in general, the concept of open science strengthens the departure from traditional metrics for evaluating research results by articles, taking into account the impact factors of journals, since this conflicts with its basic principle of expanding information openness for society. From this point of view, publications in national languages, presentations, as well as analytical reports can make a significant contribution. Now such materials are marginalized, they do not fit into the standard requirements for scientific results and are not taken into account in the databases on which modern research evaluation metrics are based.

    It is no coincidence, therefore, that there has been a proliferation of platforms hosting preliminary research results. An example is the British platform Octopus, which is scheduled to be fully launched this year.

    The platform is intended as a free and “fair” replacement for journal articles. Its creation is financed by the Research England Foundation, which allocates £650,000 for this purpose for three years (Morgan J. Octopus aims to grasp ‘publishing revolution science needs’ // Times Higher Education, August 12, 2021).

    The way the platform works is described as allowing researchers to post “what you do when you do it.” In Octopus, research projects are divided into several elements, which are sequentially published. For example, first you can publish scientific hypotheses, then methods / protocol, data / results, analysis, interpretations, use in practice. These elements can then be joined together to form an article. Breaking down into small fragments speeds up publication, and also shows the individual contribution of each co-author to a future article. At the same time, in order to avoid publishing low-quality materials, the developers plan to introduce a system of profiles and ratings of authors, as well as reviewing fragments after they are published.

    The approach is controversial: eliminating one problem, it creates another. Thus, the total time spent on the preparation and publication of individual parts of the work obviously exceeds the time spent on writing an article in the traditional format.

    Two aspects of open science are of great importance for Russia, since they directly affect the ethics of production and evaluation of scientific knowledge – open scientific data, and, accordingly, their reproducibility, and open review (expertise).

    The effect of “unique” science

    The problem of reproducibility arose before the concept of open science began to develop. Reproducibility is the ability to get the same result using the same set of data. The problem of reproducibility is quite serious, since researchers often cannot reproduce not only experiments or field studies based on other people’s data, but also repeat their own results. For example, a survey conducted by the editors of the journal Nature showed that about 60% of the scientists surveyed could not reproduce their own results in chemistry and biology, about 55% in medicine, and about 50% in physics (The State of Scientific Research Productivity. How to Sustain a Critical Engine of Human Progress, Oxford Economics, November 2021, p. 35).

    Initially, reproducibility was considered in the context of the fact that journals are not inclined to accept for publication articles where an experiment is repeated, the results of which have already been described by someone. Therefore, one cannot be sure that previously published results are indeed correct. This creates the risk of publishing erroneous conclusions, which has already happened even in such prestigious journals as Science and Nature. The requirement to provide original data solves this problem, since it becomes possible to repeat past experiments.

    So far, the policy of open data is hotly debated. Opponents believe that this may lead to the curtailment of cooperation, including international, because access to data will no longer require establishing personal contacts.

    Proponents of data discovery, on the other hand, believe that links between researchers will develop to share this data.

    And in the scientific community there is no great desire to open the data of their research. For example, as an analysis of the work of scientific collaborations on the topic of COVID-19 showed, only 9% of the articles contained appendices with source data (Grove J. Data sharing on Covid research ‘disappointing’, says EU chief // Times Higher Education, May 26, 2021). On other topics, the share of articles with open data was even smaller – about 1% of their total number.

    Reproducibility is closely related to the issue of scientific integrity. Intentional deception (data tampering) is not common, but there are honest technical errors, equipment miscalibration, contamination or degradation of experimental materials. All this can lead to erroneous data.

    In chemistry and biology, they could not reproduce
    own results about 60%
    of surveyed scientists, in medicine – about 55%,
    physics – about 50%. Pexels Photos

    The transition to open science, when the requirements for initial data are growing, helps to ensure that “scientific integrity” becomes the norm. But in the current conditions of the organization of science, this may increase the contradiction between “quality work” and “successful career.” If you spend more time doing everything carefully and transparently, then there will be fewer publications. In the meantime, the system is designed in such a way that career advancement and financial well-being largely depend on formal indicators, including scientometric indicators.

    To resolve this contradiction, in addition to changing formal assessment systems, some countries are beginning to experiment with different financial incentives to increase “scientific integrity”. So, from 2021, through the UK Reproducibility Network, over five years, £8.5 million will be distributed to encourage open research methods and reproducibility of results (Grove J. UK reproducible science project wins £8.5 million // Times Higher Education, September 15, 2021).

    In Russia, the problem of scientific integrity is very acute, and its seriousness grows as the publication race intensifies. The growing variety of unfair publishing practices inevitably affects the quality of the data used. The Dissernet community gives many vivid examples of the repeated use (borrowing) of other people’s data, accompanied by unusual transformations of entities – looms into laboratory stands or schoolchildren into judges (L. Melikhova. Dissernet’s calendar: the most ridiculous cases // Troitsky variant-science, 21 April 2020).

    It is also important that the transition to open data more accurately shows the individual contribution of researchers. This can reduce the percentage of “parasitic co-authors” (the so-called guest, honorary, purchased and other co-authorships).

    Open Review

    Another type of open science is open peer review.

    There are over 20 definitions of what open peer review is, but three interpretations are most commonly used. First, opening the names of the reviewer and the author of the application / text of the article to each other. Secondly, “open reviews” (reviewers’ responses are published along with the article). The third interpretation implies open participation in peer review, when the evaluation of the work can be done not only by invited experts, but also by external experts. (This is sometimes referred to as crowdsourced peer review.)

    In the latter case, confirmation of the qualifications of such an external expert may be required. For example, the online publication Science Open requires an ORCID profile (Open Researcher and Contributor ID) and at least five published articles. The purpose of open participation is to reduce the conflict associated with the selection of reviewers. This is supposed to improve the quality of peer review.

    Open peer review is actively discussed because the traditional system of expertise (peer review) has become increasingly criticized. There are indeed many vulnerabilities in it, such as biased reviewers, favoring friends, inconsistency of opinions, unethical behavior, up to the theft of ideas or writing deliberately negative reviews of the work of competitors (Smith R. Peer Review: a Flawed Process at the Heart of Science and Journals. JR Soc Med SAGE Publications 2006;99(4): 178–82). At the same time, the anonymity of the authors, when it is observed, is conditional: in almost half of the cases, reviewers can identify them (Godlee F, Gale CR, Martyn CN. Effect on the quality of peer review of blinding reviewers and asking them to sign their reports: a randomized controlled trial JAMA American Medical Association (AMA) 1998 280 (3): 237–40).

    Finally, reviewers have no external incentives for quality work – it is not rewarded in any way. In general, the more frequent cases of retraction (withdrawal from publication) of articles indicate that the examination does not solve its tasks well.

    Open peer review can theoretically lead to an increase in the quality of peer review, since the reviewers will be more motivated and attentive when writing their expert opinions. At the same time, however, there will be a reduction in quality, as reviewers may begin to hide their real opinion in order to avoid conflicts. This is especially true for works, among the co-authors of which there are senior administrators.

    For authors, a separate danger is that criticism of their work in the press will affect career prospects.

    In Russia, in addition to the above, there is another factor of difficulty in selecting reviewers, especially in narrow and interdisciplinary areas. However, it can be assumed that open peer review will make the choice of experts more adequate and those who are not experts in the subject under review will not be taken to evaluate scientific papers.

    Who will pay for open science

    For Russian science to become part of the open, it will take a lot of effort. But if organizational and financial changes can be made within a reasonably reasonable timeframe, changing ethical standards will be much more difficult. This, among other things, will require abandoning the current paradigm of scientometric evaluation of scientific results and career advancement in science.

    However, the initial impetus to embedding in the practices of open science can be given precisely by the requirements for reporting on budget financing, which, among other things, establish publication standards. Under the rules of science foundations and a number of programs (for example, Priority 2030), the emphasis on reporting on the number of articles in journals in the first and second quartiles that are actively transitioning to open access is increasing. This means that the ways of financing such publications must change. Therefore, it is necessary to find ways of budgetary support for the publications of Russian scientists in open access journals.

    The question of the sources of financing for those works that are carried out outside the framework of grants and government assignments will become more acute. Will universities and institutes have funds for this? The cost of an open access publication varies on average between $3,000 and $5,000. Russian scientists are unlikely to pay for it out of their own income.

    With the rest of the elements of open science, it will be much more difficult, since a serious transformation of the very principles of research will become inevitable. Including increasing the ethical standards for the use of data, changing the protocols for conducting experiments and field studies. All this will have to be learned. However, it is the movement towards openness that can increase the value of professional reputation.

    Report from breakfast with Charles Weatherell, author of the cult book “Etudes for Programmers” / Sudo Null IT News

    Breakfast with Charles Weatherell, author of the cult book “Etudes for Programmers”, dragged on for four hours. In the end, the waitress asked us from a restaurant in Palo Alto, saying that there was a long line to the restaurant, and we have been sitting here since eight in the morning. During this time, we discussed a lot of interesting things: Charles’s work at the Livermore Laboratory and Oracle, object-oriented and functional programming, compilers and hardware description languages, bookmarks in processors, inefficiency of neural networks and an undeservedly forgotten Prolog, Charles’s visit to Russia, text processing by a state machine in a hardware coprocessor and the creation of video games by schoolchildren on FPGAs.

    The content of four hours with Charles Weatherell is enough for fifty articles on Habré, so I will list the main topics, after which I will give some details about three of them:

    1. Object-oriented and functional programming. Single assignment, function values, get rid of mutations, get rid of timing.
    2. Data structures and compiler algorithms. Muchnik SSA and a book on optimizations. Bob Morgan (Compass) building optimizing compilers. Vectorizing compilers and Randy Allen (my colleague at Wave and Charles’ colleague at other companies).
    3. Synopsys evolution of the Yacc parser, Ada (DIANA) language internals, and VHDL frontend.
    4. Attributive grammars and unsuccessful, in my opinion, their use in the MIPT training manual on the Theory of Implementation of Programming Languages ​​(TRYAP).
    5. JOVIAL programming language and Ada standardization. IDL language.
    6. Programming at the Livermore Computing Laboratory for Physicists and Chemists on the CDC 7600 and Cray-1. Livermore Fortran is an extension of Fortran-77 with structures and dynamic memory allocation. The use of microfiches, including for automatic search and production of animations. Harry Nelson. And how the Rubik’s cube got into the laboratory before it became known.
    7. Soviet clone of Cray-1 Elektronika SS BIS. The Fortran compiler at IPM and the C compiler that we worked on at MIPT.
    8. Reverse engineering the random number generator in Synopsys VCS. Congruential generator with register shift. LSFR.
    9. The inefficiency of neural networks and the undeservedly forgotten Prolog language.
    10. Application of methods from Prolog for static analysis of program text.
    11. Including analysis of the processor code written in Verilog or VHDL in order to find bookmarks in it. A bookmark scattered across different parts of the processor description at the level of register transfers. Finding “extra” code that does something outside the specification. For example, a state machine that waits for a key phrase, text in registers visible to the programmer, and then puts the processor in privileged mode.
    12. Hybrid code analysis methods – dynamic execution followed by static state space exploration from some execution point.
    13. Hakmem list from MIT.
    14. Most programmers in their life use only five algorithms – quick sort, binary search, hashing, list insertion, and something else (AVL binary tree insertion?).
    15. History of Unix troff at Bell Labs.
    16. Charles Weatherell’s Oracle work on SQL.
    17. A good example of using a hardware coprocessor for MIPS CorExtend / UDI is User Defined Instructions. Adding instructions to the processor for fast lexical analysis, with a state machine inside the coprocessor and saving state between individual instructions. Background from the days of IBM/360 translate test and CDC STAR.
    18. Using a hardware coprocessor to preclean a data stream before applying machine learning algorithms to it.
    19. Game Rogue, Scientific American in the states and the USSR.
    20. Summer School for Young Programmers in Novosibirsk and mosquitoes in it (according to my recollections and stories of colleagues Charles Weatherell)
    21. How Charles spent 36 hours in Moscow and two weeks in St. Petersburg. Hermitage. He did not read lectures in St. Petersburg universities.
    22. Suggested that Charles go to summer school at MIET/Zelenograd in July or somewhere else in the fall (MSU? MIPT? ITMO?).
    23. Teaching schoolchildren and younger students. The need to get out of a pattern (for example, sequential programming) and learning Verilog on an FPGA as one way to get out of such a pattern.
    24. Using microcircuits with a low degree of integration before FPGA exercises, so that a schoolchild or student intuitively understands that the Verilog code is a description of an electronic circuit, and not a program (chain of instructions).
    25. Example for RTL on FPGA for summer school at MIET / Zelenograd in July – a self-learning state machine that calculates the opponent’s tendencies in the game “rock-paper-scissors”.
    26. Another example is the competition of finite automata (animals) that move the player to the goal on the map (globe). Objects on the map have a “smell” – positive (food) or negative (electricity that can hit). Designing a map in FPGA, output and player sprite on VGA using the scan generation module.

    Here we analyzed the recent disputes on Habré about OOP. Charles campaigns for both OOP and functional programming where applicable. I showed Charles an example of poor class design that I saw in two projects to represent nodes of a parse tree and optimizations on this tree, after which Charles said that of course tree transformation algorithms should not be scattered over small classes in this way, but instead a parse tree should be quickly transfer to the control flow graph, on which to use table driven transformations based on static single assignment, with a small number of exceptions. Charles enlightened me on the work of Muchnik, Bob Morgan and Randy Allen on vectorization:

    Then I told Charles that the day after tomorrow we at the company will be holding a seminar in Las Vegas at the Electronic Design Automation conference, and I need his advice on a good example of a coprocessor based on the CorExtend / UDI protocol – User Defined Instructions. This protocol is used in MIPS cores. CorExtend/UDI allows you to embed a block in the processor that decodes and executes instructions that are additional to the main instruction set, which can be defined by the system-on-chip designer. The block can be synthesized and become part of a chip or be configured in an FPGA/FPGA.

    Additional instructions move along the processor pipeline along with the main ones. They receive data from programmer-visible general-purpose registers and can return the result to a register. These instructions can also store some state in the coprocessor. They can be killed with exceptions if an exception occurs, for example, in the following instruction in the pipeline:

    The day after tomorrow, in the presentation at the seminar, I am going to use an example with a simple convolution instruction for a neural network. But the acceleration achieved in this case is not impressive – only twice. Could you make a better example?

    Charles immediately came up with a much better example: hardware lexical analysis. You can put a state machine in the coprocessor that will determine the numbers, identifiers, and comments in the text stream. It will save state between individual instructions that pass text from registers to the automaton. The result of the current analysis (marked text) will also be returned to the register.

    Charles also told me the history of text parsing instructions from the days of IBM/360 translate test and CDC STAR. He also told me that such a coprocessor can be used for machine learning, to pre-clean the data stream before applying machine learning algorithms to it.

    Then I told Charles the saga how a group of engineers and teachers translated and implemented in various Russian universities the textbook “Digital Circuitry and Computer Architecture” by David Harris and Sarah Harris (see posts on Habré 1, 2, 3). Now, with the combined efforts of MIET, RUSNANO, teachers from MEPhI and other universities, we are planning a summer school at MIET where advanced students design video games on FPGAs with display on a graphic screen (section Between physics and programming).