Fast and Flexible FPGA-based NoC Hybrid Emulation


Researchers from RWTH Aachen University and Otto-von-Guericke Universitat Magdeburg have published a new technical paper titled "EmuNoC: Hybrid Emulation for Fast and Flexible Network-on-Chip Prototyping on FPGAs." Abstract: "Networks-on-Chips (NoCs) recently became widely used, from multi-core CPUs to edge-AI accelerators. Emulation on FPGAs promises to accelerate their RTL modeling co... » read more

SW/HW Framework for for GASNet-enabled FPGA Hardware Acceleration Infrastructure


Researchers from KAIST and Flapmax published a new technical paper titled "FSHMEM: Supporting Partitioned Global Address Space on FPGAs for Large-Scale Hardware Acceleration Infrastructure." Abstract "By providing highly efficient one-sided communication with globally shared memory space, Partitioned Global Address Space (PGAS) has become one of the most promising parallel computing model... » read more

A Safety-Oriented System Hardware Architecture Exploration Framework


New technical paper titled "Safety-Oriented System Hardware Architecture Exploration in Compliance with ISO 26262" from researchers at National Taipei University. Abstract: "Safety-critical intelligent automotive systems require stringent dependability while the systems are in operation. Therefore, safety and reliability issues must be addressed in the development of such safety-critical sy... » read more

MIT & UC Berkeley: “Exo” Programming Language Writes High Performance Code For HW Accelerators


New research paper titled "Exocompilation for productive programming of hardware accelerators," from researchers at MIT and UC Berkeley. From their abstract: "To better support development of high-performance libraries for specialized hardware, we propose a new programming language, Exo, based on the principle of exocompilation: externalizing target-specific code generation support and op... » read more

ISA Extension For Low-Precision NN Training On RISC-V Cores


New technical paper titled "MiniFloat-NN and ExSdotp: An ISA Extension and a Modular Open Hardware Unit for Low-Precision Training on RISC-V cores" from researchers at IIS, ETH Zurich; DEI, University of Bologna; and Axelera AI. Abstract "Low-precision formats have recently driven major breakthroughs in neural network (NN) training and inference by reducing the memory footprint of the N... » read more

3 Emerging Technologies: Memristors, Spintronics & 2D Materials


New technical paper titled "Memristive, Spintronic, and 2D-Materials-Based Devices to Improve and Complement Computing Hardware" from researchers at University College London and University of Cambridge. Abstract "In a data-driven economy, virtually all industries benefit from advances in information technology—powerful computing systems are critically important for rapid technological pr... » read more

Neuromorphic Computing: Challenges, Opportunities Including Materials, Algorithms, Devices & Ethics


This new research paper titled "2022 roadmap on neuromorphic computing and engineering" is from numerous researchers at Technical University of Denmark, Instituto de Microelectrónica de Sevilla, CSIC, University of Seville, and many others. Partial Abstract: "The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the chall... » read more

Brain-Inspired Computing Device That Programs/RePrograms HW On Demand With Electrical Pulses


Multiple academic and government institutions jointly developed a new computer device that can "program and program computer hardware on demand through electrical pulses," according to this Argonne National Lab news release. The device's key materials are neodymium, nickel and oxygen and is referred to as a perovskite nickelate. This new research paper titled "Reconfigurable perovskite nicke... » read more

Sibyl, a lightweight, reinforcement learning-based data placement technique for hybrid storage systems (ETH Zurich)


New research paper titled "Sibyl: Adaptive and Extensible Data Placement in Hybrid Storage Systems Using Online Reinforcement Learning" from researchers at ETH Zurich, Eindhoven University of Technology, and LIRMM, Univ. Montpellier, CNRS. Abstract "Hybrid storage systems (HSS) use multiple different storage devices to provide high and scalable storage capacity at high performance. Recent r... » read more

MEMprop: Gradient-based Learning To Train Fully Memristive SNNs


New technical paper titled "Gradient-based Neuromorphic Learning on Dynamical RRAM Arrays" from IEEE researchers. Abstract "We present MEMprop, the adoption of gradient-based learning to train fully memristive spiking neural networks (MSNNs). Our approach harnesses intrinsic device dynamics to trigger naturally arising voltage spikes. These spikes emitted by memristive dynamics are anal... » read more

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