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Arbitrary Precision DNN Accelerator Controlled by a RISC-V CPU (Ecole Polytechnique Montreal, IBM, Mila, CMC)


A new technical paper titled "BARVINN: Arbitrary Precision DNN Accelerator Controlled by a RISC-V CPU" was written by researchers at Ecole Polytechnique Montreal, IBM, Mila and CMC Microsystems. It was accepted for publication in the 2023, 28th Asia and South Pacific Design Automation Conference (ASP-DAC 2023) in Japan. Abstract: "We present a DNN accelerator that allows inference at arbitr... » read more

MTJ-based Circuits Provide Low-Cost, Energy Efficient Solution For Future Hardware Implementation in SC Algorithms


A review paper titled "Review of Magnetic Tunnel Junctions for Stochastic Computing" was published by researchers at University of Minnesota Twin Cities. Funding agencies include Semiconductor Research Corporation (SRC), CAPSL, NIST, DARPA and others. Abstract: "Modern computing schemes require large circuit areas and large energy consumption for neuromorphic computing applications, such as... » read more

Neural Architecture & Hardware Accelerator Co-Design Framework (Princeton/ Stanford)


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

2D-Materials-Based Electronic Circuits (KAUST and TSMC)


A special edition article titled "Electronic Circuits made of 2D Materials" was just published by Dr. Mario Lanza, KAUST Associate Professor of Material Science and Engineering, and Iuliana Radu, corporate researcher at TSMC. This special issue covers 21 articles from leading subject matter experts, ranging from materials synthesis and their integration in micro/nano-electronic devices and c... » read more

Optimizing Hardware Capacity, Utilizing Automatic Differentiation to Efficiently Compute Derivatives in Parallel Programming Models


A technical paper titled "Scalable Automatic Differentiation of Multiple Parallel Paradigms through Compiler Augmentation" was published by researchers at MIT (CSAIL), Argonne National Lab, and TU Munich. The paper was a Best Paper Finalist and a Best Student Paper winner at SuperComputing 2022. Find the technical paper here. Published November 2022. The work "demonstrates how Enzyme opti... » read more

Profile-Guided HW/SW Mechanism To Efficiently Reduce Branch Mispredictions In Data Center Applications (Best Paper Award)


A new technical paper titled "Whisper: Profile-Guided Branch Misprediction Elimination for Data Center Applications" was published by researchers at University of Michigan, ARM, University of California, Santa Cruz, and Texas A&M University. This work was awarded a best paper award at October's 2022 Institute of Electrical and Electronics Engineers (IEEE)/Association for Computing Machin... » read more

Memory and Energy-Efficient Batch Normalization Hardware


A new technical paper titled "LightNorm: Area and Energy-Efficient Batch Normalization Hardware for On-Device DNN Training" was published by researchers at DGIST (Daegu Gyeongbuk Institute of Science and Technology). The work was supported by Samsung Research Funding Incubation Center. Abstract: "When training early-stage deep neural networks (DNNs), generating intermediate features via con... » read more

Multi-Bit In-Memory Computing System for HDC using FeFETs, Achieving SW-Equivalent-Accuracies


A new technical paper titled "Achieving software-equivalent accuracy for hyperdimensional computing with ferroelectric-based in-memory computing" by researchers at University of Notre Dame, Fraunhofer Institute for Photonic Microsystems, University of California Irvine, and Technische Universität Dresden. "We present a multi-bit IMC system for HDC using ferroelectric field-effect transistor... » read more

Scalable Optical AI Accelerator Based on a Crossbar Architecture


A new technical paper titled "Scalable Coherent Optical Crossbar Architecture using PCM for AI Acceleration" was published by researchers at University of Washington. Abstract: "Optical computing has been recently proposed as a new compute paradigm to meet the demands of future AI/ML workloads in datacenters and supercomputers. However, proposed implementations so far suffer from lack of sc... » read more

Using BDA To to Predict SAQP Pitch Walk


A new technical paper titled "Bayesian dropout approximation in deep learning neural networks: analysis of self-aligned quadruple patterning" was published by researchers at IBM TJ Watson Research Center and Rensselaer Polytechnic Institute. Find the technical paper here. Published November 2022.  Open Access. Scott D. Halle, Derren N. Dunn, Allen H. Gabor, Max O. Bloomfield, and Mark Sh... » read more

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