tag: machine learning

Neural Architecture & Hardware Accelerator Co-Design Framework (Princeton/ Stanford)
By Technical Paper Link - 12 Dec, 2022 - Comments: 0

A new technical paper titled "CODEBench: A Neural Architecture and Hardware Accelerator Co-Design Framework" was published by researchers at Princeton University and Stanford University. "Recently, automated co-design of machine learning (ML) models and accelerator architectures has attracted significant attention from both the industry and academia. However, most co-design frameworks either... » read more

Don’t Let Your ML Accelerator Vendor Tell You The ‘F-Word’
By Steve Roddy - 08 Dec, 2022 - Comments: 0

Machine learning (ML) inference in devices is all the rage. Nearly every new system on chip (SoC) design start for mobile phones, tablets, smart security cameras, automotive applications, wireless systems, and more has a requirement for a hefty amount of ML capability on-chip. That has silicon design teams scrambling to find ML processing power to add to the existing menu of processing engines ... » read more

Systematic Yield Issues Now Top Priority At Advanced Nodes
By Laura Peters - 06 Dec, 2022 - Comments: 0

Systematic yield issues are supplanting random defects as the dominant concern in semiconductor manufacturing at the most advanced process nodes, requiring more time, effort, and cost to achieve sufficient yield. Yield is the ultimate hush hush topic in semiconductor manufacturing, but it's also the most critical because it determines how many chips can be profitably sold. "At older nodes, b... » read more

Automated Optical Inspection
By Ed Sperling - 07 Nov, 2022 - Comments: 0

Building good automated models for inspection require more data to be collected, both good and bad. Vijay Thangamariappan, R&D engineer at Advantest, explains how to develop models for automating optical inspection, using a multi-thousand pin socket as an example for how machine learning has helped reduce the return rate due to defects from 2% down to zero. He also explains how to achieve t... » read more

Complex Tradeoffs In Inferencing Chips
By John Koon - 17 Oct, 2022 - Comments: 0

Designing AI/ML inferencing chips is emerging as a huge challenge due to the variety of applications and the highly specific power and performance needs for each of them. Put simply, one size does not fit all, and not all applications can afford a custom design. For example, in retail store tracking, it's acceptable to have a 5% or 10% margin of error for customers passing by a certain aisle... » read more

Training a ML model On An Intelligent Edge Device Using Less Than 256KB Memory
By Technical Paper Link - 11 Oct, 2022 - Comments: 0

A new technical paper titled "On-Device Training Under 256KB Memory" was published by researchers at MIT and MIT-IBM Watson AI Lab. “Our study enables IoT devices to not only perform inference but also continuously update the AI models to newly collected data, paving the way for lifelong on-device learning. The low resource utilization makes deep learning more accessible and can have a bro... » read more

More Efficient Matrix-Multiplication Algorithms with Reinforcement Learning (DeepMind)
By Technical Paper Link - 11 Oct, 2022 - Comments: 0

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

Simplifying AI Edge Deployment
By Ed Sperling - 10 Oct, 2022 - Comments: 0

Barrie Mullins, vice president of product at Flex Logix, explains how a programmable accelerator chip can simplify semiconductor design at the edge, where chips need to be high performance as well as low power, yet developing everything from scratch is too expensive and time-consuming. Programmability allows these systems to stay current with changes in algorithms, which can affect everything f... » read more

Speeding-Up Thermal Simulations Of Chips With ML
By Technical Paper Link - 02 Oct, 2022 - Comments: 0

A new technical paper titled "A Thermal Machine Learning Solver For Chip Simulation" was published by researchers at Ansys. Abstract "Thermal analysis provides deeper insights into electronic chips' behavior under different temperature scenarios and enables faster design exploration. However, obtaining detailed and accurate thermal profile on chip is very time-consuming using FEM or CFD. Th... » read more

Can ML Help Verification? Maybe
By Brian Bailey - 22 Sep, 2022 - Comments: 0

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

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