Gold Substrate Plays Boosts Performance of Tellurium-Based Memristors


A new technical paper titled "Non-Volatile Resistive Switching in Nanoscaled Elemental Tellurium by Vapor Transport Deposition on Gold" was published by researchers at Politecnico di Milano, UT Austin, and STMicroelectronics. Abstract: "Two-dimensional (2D) materials are promising for resistive switching in neuromorphic and in-memory computing, as their atomic thickness substantially impr... » read more

Fine-Grained Functional Partitioning For Low Level SRAM Cache in 3D-IC designs (imec)


A new technical paper titled "Towards Fine-grained Partitioning of Low-level SRAM Caches for Emerging 3D-IC Designs" was published by researchers at imec. "We propose a partitioning of low-level (faster access) caches in 3D using an Array Under CMOS (AuC) technology paradigm. Our study focuses on partitioning and optimization of SRAM bit-cells and peripheral circuits, enabling heterogeneous ... » read more

Memristors: Flexible Behavioral Model ( Israel Institute of Technology)


A new technical paper titled "VVTEAM: A Compact Behavioral Model for Volatile Memristors" was published by researchers at Technion – Israel Institute of Technology. Abstract "Volatile memristors have recently gained popularity as promising devices for neuromorphic circuits, capable of mimicking the leaky function of neurons and offering advantages over capacitor-based circuits in terms of... » read more

Novel NorthPole Architecture Enables Low-Latency, High-Energy-Efficiency LLM inference (IBM Research)


A new technical paper titled "Breakthrough low-latency, high-energy-efficiency LLM inference performance using NorthPole" was published by researchers at IBM Research. At the IEEE High Performance Extreme Computing (HPEC) Virtual Conference in September 2024, new performance results for their AIU NorthPole AI inference accelerator chip were presented on a 3-billion-parameter Granite LLM. ... » read more

Hardware Acceleration Approach for KAN Via Algorithm-Hardware Co-Design


A new technical paper titled "Hardware Acceleration of Kolmogorov-Arnold Network (KAN) for Lightweight Edge Inference" was published by researchers at Georgia Tech, TSMC and National Tsing Hua University. Abstract "Recently, a novel model named Kolmogorov-Arnold Networks (KAN) has been proposed with the potential to achieve the functionality of traditional deep neural networks (DNNs) using ... » read more

Flexible IGZO RISC-V Microprocessor


A new technical paper titled "Bendable non-silicon RISC-V microprocessor" was published by researchers at Pragmatic Semiconductor, Qamcom,  and Harvard University. From the abstract: "Here we present Flex-RV, a 32-bit microprocessor based on an open RISC-V instruction set fabricated with indium gallium zinc oxide thin-film transistors on a flexible polyimide substrate, enabling an ultralow... » read more

Characteristics and Potential HW Architectures for Neuro-Symbolic AI


A new technical paper titled "Towards Efficient Neuro-Symbolic AI: From Workload Characterization to Hardware Architecture" was published by researchers at Georgia Tech, UC Berkeley, and IBM Research. Abstract: "The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural networks, are facing challenges surrounding unsustainable computational trajectories, li... » read more

Mixed Signal In-Memory Computing With Massively Parallel Gradient Calculations of High-Degree Polynomials


A new technical paper titled "Computing high-degree polynomial gradients in memory" was published by researchers at UCSB, HP Labs, Forschungszentrum Juelich GmbH, and RWTH Aachen University. Abstract "Specialized function gradient computing hardware could greatly improve the performance of state-of-the-art optimization algorithms. Prior work on such hardware, performed in the context of Isi... » read more

Energy-Efficient DRAM↔PIM Transfers for PIM Systems (KAIST)


A new technical paper titled "PIM-MMU: A Memory Management Unit for Accelerating Data Transfers in Commercial PIM Systems" was published by researchers at KAIST. Abstract "Processing-in-memory (PIM) has emerged as a promising solution for accelerating memory-intensive workloads as they provide high memory bandwidth to the processing units. This approach has drawn attention not only from the... » read more

Analog In-Memory Computing: Fast Deep NN Training (IBM Research)


A new technical paper titled "Fast and robust analog in-memory deep neural network training" was published by researchers at IBM Research. Abstract "Analog in-memory computing is a promising future technology for efficiently accelerating deep learning networks. While using in-memory computing to accelerate the inference phase has been studied extensively, accelerating the training phase has... » read more

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