Novel In-Pixel-in-Memory (P2M) Paradigm for Edge Intelligence (USC)


A new technical paper titled "A processing-in-pixel-in-memory paradigm for resource-constrained TinyML applications" was published by researchers at University of Southern California (USC). According to the paper, "we propose a novel Processing-in-Pixel-in-memory (P2M) paradigm, that customizes the pixel array by adding support for analog multi-channel, multi-bit convolution, batch normaliza... » 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

Cloud-Ready Circuit Simulation Accelerates SoC Verification


By Nebabie Kebebew and Nigel Bleasdale Driven by the explosion of big data and expanding applications, chip design complexity is increasing. Applications such as high-performance computing (HPC), the Internet of Things (IoT), automotive, and 5G mobile and communications coupled with advanced process technology nodes require running a large number of circuit simulations to ensure the circuits... » read more

Signoff-Accurate Partial Layout Extraction And Early Simulation


It is a rewarding experience for EDA developers and users to collaborate on deploying advanced techniques to improve design productivity. This blog will describe the experience of collaborating with customers on a new technology for reducing the number of analog design iterations. Analog design requires that engineers balance the needs to 1) reach market quickly 2) deliver high quality 3) at lo... » read more

Analog Deep Learning Processor (MIT)


A team of researchers at MIT are working on hardware for artificial intelligence that offers faster computing with less power. The analog deep learning technique involves sending protons through solids at extremely fast speeds.  “The working mechanism of the device is electrochemical insertion of the smallest ion, the proton, into an insulating oxide to modulate its electronic conductivity... » read more

The Reliability Of Analog Integrated Circuits And Their Simulation-Aided Verification


Different challenges have to be overcome when designing integrated circuits. Besides schematic and layout design work, verification in view of the non-ideal behavior of circuits and semiconductor technologies in particular is also relevant. The designed circuits have to work at specific operating voltages and within ambient temperature ranges and be robust in terms of process fluctuations ... » read more

IC Reliability Burden Shifts Left


Chip reliability is coming under much tighter scrutiny as IC-driven systems take on increasingly critical and complex roles. So whether it's a stray alpha particle that flips a memory bit, or some long-dormant software bugs or latent hardware defects that suddenly cause problems, it's now up to the chip industry to prevent these problems in the first place, and solve them when they do arise. ... » read more

High-Performance 5G IC Designs Need High-Performance Parasitic Extraction


By Karen Chow and Salma Ahmed Elhenedy We are rapidly approaching a future where 5G telecommunications will be the norm. With its increased data speeds and bandwidth, 5G has the potential to change the way we live our lives. But what does that mean for the average person? Think about cellphones, for one. You don't just use your phone for calling or texting anymore—you surf the web, chec... » read more

Simulation Framework to Evaluate the Feasibility of Large-scale DNNs based on CIM Architecture & Analog NVM


Technical paper titled "Accuracy and Resiliency of Analog Compute-in-Memory Inference Engines" from researchers at UCLA. Abstract "Recently, analog compute-in-memory (CIM) architectures based on emerging analog non-volatile memory (NVM) technologies have been explored for deep neural networks (DNNs) to improve scalability, speed, and energy efficiency. Such architectures, however, leverage ... » read more

Can Analog Make A Comeback?


We live in an analog world dominated by digital processing, but that could change. Domain specificity, and the desire for greater levels of optimization, may provide analog compute with some significant advantages — and the possibility of a comeback. For the last four decades, the advantages of digital scaling and flexibility have pushed the dividing line between analog and digital closer ... » read more

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