Example of Automating Transistor-level Design
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Layman Description: Computers have been assisting humans in design of artifacts for several years now, such as design of aircraft or design of Integrated Circuits. We demonstrate one such tool that can assist engineers to build standard cells on-the-fly customized to criteria specified by them. We use genetic algorithms to guide the computer. This starts with an engineer providing the desired output signals. The computer program then randomly generates say 10,000 possible design configurations. Good designs are then Selected from this population of designs (priority given to designs that bettter meet the engineers' output requirement). Hybrid designs are developed using genetic operators. Heres where it gets interesting. Unlike most traditional computer programs, the genetic algorithm create a hybrid design by a "Crossover" of two good "Parent" designs (creating, hopefully, a better design) or random "Mutations" of good designs. These genetic operators are used on the initial population of 10,000 designs to create a second population of another (say) 10,000 designs. What happens when you do this 1000s of times? Do you arrive at the "perfect" (optimized) design? Thats what this work is about. ACKNOWLEDGEMENT This work at the AI CENTER & DEPT. OF COMPUTER SCIENCE & UNI OF GEORGIA in 2001 demonstrated designs an Inverter Standard Cell on-the-fly. It was sponsored by a YAMACRAW grant from the STATE OF GEORGIA, UNITED STATES. TECHNICAL DESCRIPTION The problem of designing the transistor-level layout of cells in a standard cell library is a multi-objective design optimization problem. Contemporary methods are optimization or compaction engines that rely on a schematic representation supplied by a design engineer. We have demonstrated the possibility of applying a modified genetic algorithm (GADO) to design a cell given only a behavioral description. A working inverter is designed as a proof of concept along with other inverters with arbitrary label placements. This is an example of design without human intervention, i.e. a computer design as opposed to a computer-aided design.
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In this talk we present lessons learned, good ideas, and thoughts on the future, with an eye toward informing junior researchers about the realities and opportunities of a long-running project. We highlight some notions from the original paper that stood the test of time, some that were not as prescient, and some that became more relevant as industrial practice advanced. We place the work in context, highlighting perceptions from software engineering and evolutionary computing, then and now, of how program repair could possibly work. We discuss the importance of measurable benchmarks and reproducible research in bringing scientists together and advancing the area. We give our thoughts on the role of quality requirements and properties in program repair. From testing to metrics to scalability to human factors to technology transfer, software repair touches many aspects of software engineering, and we hope a behind-the-scenes exploration of some of our struggles and successes may benefit researchers pursuing new projects.
universal laws.ppt
The document discusses the field of control and dynamical systems (CDS) and its goal of understanding universal laws and architectures across diverse applications and mathematics. CDS aims to provide a "core" interface between domain experts and diverse tools/mathematics, and between mathematics and diverse application drivers. It explores architectural concepts like constraints that deconstrain and universal organizational strategies to flexibly achieve performance. The document also compares CDS to other fields like complexity science and envisions CDS one day achieving deep, universal impact across applications, mathematics and practical problems.
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Incremental Model Queries for Model-Dirven Software Engineering
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Parallelization of Yeast Diode and Implementation of a Concentration Gradient
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More from Anil Bahuman
Jio's competitiveness at the Global level
This is a paper for the general public.
Content copyright.belongs to Wasim Rahman of
Indian Institute of Technology Bombay who presented this at International Conference on Management of Technology 2020 Cairo, Sep 2020.
aAQUA Case study for IRMA
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- It began in 2003 as a web portal launched by IIT Bombay to answer farmers' questions. Local farmers' questions were collected and answered by experts from the Krishi Vigyan Kendra agricultural university.
- Over time, it expanded with support from various organizations and grew to answer questions from multiple states. Mobile access was also added to make it more accessible to farmers.
TiE IITB - Jun 11 talk on Lean Startup Tactics
This document provides an overview of Lean Startup methodology and alternatives. It discusses:
- What Lean Startup does not cover and the origins of the approach.
- Key Lean Startup concepts like minimum viable products, continuous development, split testing, and pivoting based on metrics.
- Potential limitations including that it may annoy customers and investors if taken too far, and that it is not applicable to all businesses.
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How telecom spread to remote areas in India
This document discusses communications technologies that can provide internet access in rural India. It notes that currently there are 40 million fixed phone lines, 40 million mobile connections, and 5 million internet connections in India. It then describes various technologies that have been deployed in rural areas, including C-DOT switches, wireless local loop systems, satellite phones, and wireless networks using TDMA and 802.11b standards. It discusses field deployments of these technologies in remote villages in India and Nepal. Finally, it outlines objectives of providing urban-like communication features in rural areas at low cost using appropriate technologies like wireless networks, satellite modems, and WiMax.
Weather Measurement built into the SmartPhone
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UiPath Test Automation using UiPath Test Suite series, part 6
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UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
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What will you get from this session?
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3. Practical demonstrations
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During the hour, we’ll take you through:
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
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van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.Essentials of Automations: The Art of Triggers and Actions in FME
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Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
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National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
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Example of Automating Transistor-level Design
- 1. An Evolutionary approach to Standard Cell Design: a proof of concept Anil Bahuman Artificial Intelligence Center University of Georgia July 2001
- 5. Research Goal Truth Table Layout with “minimum” area “ Schematic and layout are designed in parallel” Explore possibility of automating the design of simple logic standard cells by exploring design spaces not considered by human designers 1 0 1 0 0 1 Out In
- 8. A Transistor in MAGIC
- 9. Error tiles indicating DRC Errors
- 10. An Inverter in MAGIC Lambda, cell limits, labels, template, inputs, output, gate, terminals, wires, poly, contacts, 3I - 1O
- 11. SPICE simulation of the Inverter
- 12. Research Goal
- 13. Research Goal – Inverter Truth Table Layout with “minimum” area “ Schematic and layout are designed in parallel” 1 0 1 0 0 1 Out In
- 15. Architecture
- 16. architecture MAGIC SPICE Modified GADO (DRC ERRORS , AREA) (CORRECTNESS) DESIGN ENGINE EVALUATOR DESIGN RULE CHECKER CIRCUIT SIMULATOR Fitness module (CIRCUIT FILE)
- 20. Encoding an Object A B 14 0 Cell limits Y 15 0 Cell limits X 5 0 Cell limits Stretch Factor 2 0 0-3 Orientation 11 11 1-15 Object Type B A VALUE PARAMETER
- 21. An Individual Y X Stretch Factor Orientation Object Type
- 22. Connections b/w Transistors terminal
- 25. Sample Cell Corresponding Graph
- 26. Results
- 27. Success 1
- 28. Success 2
- 29. Success 3
- 30. Evolving 1
- 31. Evolving 2
- 32. Evolving 3
- 33. Evolving 4-7 4 6 5 7
- 34. Evolving 8: Aha! Inverter
- 35. Evolving – Success 1
- 39. Merci Danke Sas efharisto Mahalo Merci Dhanyavaad Arigato Vandane Wneeweh Shukran