resistive random-access memory
PulseAugur coverage of resistive random-access memory — every cluster mentioning resistive random-access memory across labs, papers, and developer communities, ranked by signal.
4 day(s) with sentiment data
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New toolkit optimizes neural network inference on RRAM crossbars
Researchers have developed CIM-Explorer, a new toolkit designed to optimize the performance of Binary and Ternary Neural Networks (BNNs and TNNs) when run on Resistive Random-Access Memory (RRAM) crossbars. This tool ad…
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New framework optimizes ML workload partitioning for CPU-CIM systems
Researchers have developed a new framework for partitioning machine learning workloads between central processing units (CPUs) and Computing-in-Memory (CIM) accelerators. This framework addresses limitations in existing…
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New framework ApproxHDC optimizes Hyperdimensional Computing with compiler-driven approximations
Researchers have developed ApproxHDC, a novel framework that leverages compiler-driven approximation tuning to enhance the efficiency of Hyperdimensional Computing (HDC) workloads. This approach is designed to address t…
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New Finetuning Method Adapts DNNs for ReRAM In-Memory Computing
Researchers have developed a new finetuning method to adapt deep neural networks for deployment on ReRAM-based in-memory computing hardware. This approach addresses the challenges of I-V non-linearity and retention erro…
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RRAM-based RBF Neuron Hardware Achieves 89.1% MNIST Accuracy
Researchers have developed a novel hardware implementation for a Radial Basis Function (RBF) neuron using Metal-Oxide Resistive RAM (RRAM) technology. This design, based on a custom Template piXeL (TXL) cell, acts as an…
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New compute-in-memory macro boosts edge AI inference efficiency
Researchers have developed E-ReCON, a novel compute-in-memory (CIM) macro designed for efficient AI inference on edge devices. This macro utilizes a compact ReRAM bitcell capable of performing multiplication for both co…