The Impact Of ML On Chip Design

Node scaling and rising complexity are increasing the time it takes to get chips out the door. At the same time, design teams are not getting larger. What is needed is a way to automate the creative process, and to not have to start every design from scratch. This is where reinforcement learning fits in, with its ability to centralize and store “tribal knowledge. Thomas Andersen, vice preside... » read more

Solving Memory Mapping Issues with Deep RL (Google)

A technical paper titled "Optimizing Memory Mapping Using Deep Reinforcement Learning" was published by Google DeepMind and Google. Abstract: "Resource scheduling and allocation is a critical component of many high impact systems ranging from congestion control to cloud computing. Finding more optimal solutions to these problems often has significant impact on resource and time savings, red... » read more

RL-Guided Detailed Routing Framework for Advanced Custom Circuits

A technical paper titled "Reinforcement Learning Guided Detailed Routing for Custom Circuits" was published by researchers at UT Austin, Princeton University, and NVIDIA. "This paper presents a novel detailed routing framework for custom circuits that leverages deep reinforcement learning to optimize routing patterns while considering custom routing constraints and industrial design rules. C... » read more

ML Automotive Chip Design Takes Off

Machine learning is increasingly being deployed across a wide swath of chips and electronics in automobiles, both for improving reliability of standard parts and for the creation of extremely complex AI chips used in increasingly autonomous applications. On the design side, the majority of EDA tools today rely on reinforcement learning, a machine learning subset of AI that teaches a machine ... » read more

EDA Makes A Frenzied Push Into Machine Learning

Machine learning is becoming a competitive prerequisite for the EDA industry. Big chipmakers are endorsing and demanding it, and most EDA companies are deploying it for one or more steps in the design flow, with plans to add much more over time. In recent weeks, the three largest EDA vendors have made sweeping announcements about incorporating ML into their tools at their respective user eve... » read more

Leveraging Multi-Agent RL for Microprocessor Design Space (Harvard, Google)

A new technical paper titled "Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration" was published by researchers at Harvard University and Google research groups. Abstract "Microprocessor architects are increasingly resorting to domain-specific customization in the quest for high-performance and energy-efficiency. As the systems grow in complexity, fine-tuning arch... » read more

More Efficient Matrix-Multiplication Algorithms with Reinforcement Learning (DeepMind)

A new research paper titled "Discovering faster matrix multiplication algorithms with reinforcement learning" was published by researchers at DeepMind. "Here we report a deep reinforcement learning approach based on AlphaZero for discovering efficient and provably correct algorithms for the multiplication of arbitrary matrices," states the paper. Find the technical paper link here. Publis... » read more

Can ML Help Verification? Maybe

Functional verification produces an enormous amount of data that could be used to train a machine learning system, but it's not always clear which data is useful or whether it can help. The challenge with ML is understanding when and where to use it, and how to integrate it with other tools and approaches. With a big enough hammer, it is tempting to call everything a nail, and just throwing ... » read more

DNN-Opt, A Novel Deep Neural Network (DNN) Based Black-Box Optimization Framework For Analog Sizing

This technical paper titled "DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networks" is co-authored from researchers at The University of Texas at Austin, Intel, University of Glasgow. The paper was a best paper candidate at DAC 2021. "In this paper, we present DNN-Opt, a novel Deep Neural Network (DNN) based black-box optimization framework for analog sizi... » read more

Effectiveness of a Reinforcement-Learning Based Dynamic Power Manager In a SW Framework

New technical paper titled "Low-Overhead Reinforcement Learning-Based Power Management Using 2QoSM" from researchers at ETH Zurich and Georgia Tech. Abstract "With the computational systems of even embedded devices becoming ever more powerful, there is a need for more effective and pro-active methods of dynamic power management. The work presented in this paper demonstrates the effectiven... » read more

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