Graph-Based, Formal Equivalence Checking Method


A new research paper titled "Equivalence Checking of System-Level and SPICE-Level Models of Linear Circuits" was published by researchers at University of Bremen and DFKI GmbH. Abstract: "Due to the increasing complexity of analog circuits and their integration into System-on-Chips (SoC), the analog design and verification industry would greatly benefit from an expansion of system-level met... » read more

Convolutional Neural Networks: Co-Design of Hardware Architecture and Compression Algorithm


Researchers at Soongsil University (Korea) published "A Survey on Efficient Convolutional Neural Networks and Hardware Acceleration." Abstract: "Over the past decade, deep-learning-based representations have demonstrated remarkable performance in academia and industry. The learning capability of convolutional neural networks (CNNs) originates from a combination of various feature extraction... » read more

New Processor Fuzzing Mechanism


Researchers from Boston University and University of Washington published a technical paper titled "ProcessorFuzz: Guiding Processor Fuzzing using Control and Status Registers." Abstract "As the complexity of modern processors has increased over the years, developing effective verification strategies to identify bugs prior to manufacturing has become critical. Undiscovered micro-architectur... » read more

Overview of Hardware-In-The-Loop (HIL) Simulations


This technical paper titled "Hardware-in-the-Loop Simulations: A Historical Overview of Engineering Challenges" was published by researchers at University of Maribor, Slovenia. Abstract: "The design of modern industrial products is further improved through the hardware-in-the-loop (HIL) simulation. Realistic simulation is enabled by the closed loop between the hardware under test (HUT) and ... » read more

Artificial Neural Network (ANN)-Based Model To Evaluate The Characteristics of A Nanosheet FET (NSFET)


This new technical paper titled "Machine-Learning-Based Compact Modeling for Sub-3-nm-Node Emerging Transistors" was published by researchers at SungKyunKwan University, Korea. Abstract: "In this paper, we present an artificial neural network (ANN)-based compact model to evaluate the characteristics of a nanosheet field-effect transistor (NSFET), which has been highlighted as a next-generat... » read more

Algorithm HW Framework That Minimizes Accuracy Degradation, Data Movement, And Energy Consumption Of DNN Accelerators (Georgia Tech)


This new research paper titled "An Algorithm-Hardware Co-design Framework to Overcome Imperfections of Mixed-signal DNN Accelerators" was published by researchers at Georgia Tech. According to the paper's abstract, "In recent years, processing in memory (PIM) based mixed-signal designs have been proposed as energy- and area-efficient solutions with ultra high throughput to accelerate DNN com... » read more

Designing for FPGA Accelerators


This research paper titled "High-Level Synthesis Hardware Design for FPGA-based Accelerators: Models, Methodologies, and Frameworks" was published by researchers at Università degli Studi di Trieste (Italy), Universidad Nacional de San Luis (Argentina), and the Abdus Salam International Centre for Theoretical Physics (Italy). According to the paper's abstract, "This paper presents a survey ... » read more

Costs of Static HW Partitioning on RISC-V


A new technical paper titled "Static Hardware Partitioning on RISC-V -- Shortcomings, Limitations, and Prospects" was published by researchers at Technical University of Applied Sciences (Regensburg, Germany) and Siemens AG (Corporate Research). Abstract "On embedded processors that are increasingly equipped with multiple CPU cores, static hardware partitioning is an established means of c... » read more

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

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