HW/SW Co-Design to Configure DNN Models On Energy Harvesting Devices


New technical paper titled "EVE: Environmental Adaptive Neural Network Models for Low-Power Energy Harvesting System" was published by researchers at UT San Antonio, University of Connecticut, and Lehigh University. According to the abstract: "This paper proposes EVE, an automated machine learning (autoML) co-exploration framework to search for desired multi-models with shared weights for... » 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

Effectiveness of a Reinforcement-Learning Based Dynamic Power Manager In a SW Framework


New technical paper titled "Low-Overhead Reinforcement Learning-Based Power Management Using 2QoSM" from researchers at ETH Zurich and Georgia Tech. Abstract "With the computational systems of even embedded devices becoming ever more powerful, there is a need for more effective and pro-active methods of dynamic power management. The work presented in this paper demonstrates the effectiven... » 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

Flip-Chip Integration of a GaSb Semiconductor Optical Amplifier with a Silicon Photonic Circuit


New research paper titled "Hybrid silicon photonics DBR laser based on flip-chip integration of GaSb amplifiers and µm-scale SOI waveguides" by researchers at Tampere University (Finland). Abstract: "The development of integrated photonics experiences an unprecedented growth dynamic, owing to accelerated penetration to new applications. This leads to new requirements in terms of functional... » read more

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