A programmable heterogeneous microprocessor based on bit-scalable in-memory computing
In-memory computing (IMC) addresses the cost of accessing data from memory in a manner
that introduces a tradeoff between energy/throughput and computation signal-to-noise ratio …
that introduces a tradeoff between energy/throughput and computation signal-to-noise ratio …
Resistive memory‐based in‐memory computing: from device and large‐scale integration system perspectives
In‐memory computing is a computing scheme that integrates data storage and arithmetic
computation functions. Resistive random access memory (RRAM) arrays with innovative …
computation functions. Resistive random access memory (RRAM) arrays with innovative …
Accurate and energy-efficient bit-slicing for RRAM-based neural networks
Computation-in-memory (CIM) paradigm leverages emerging memory technologies such as
resistive random access memories (RRAMs) to process the data within the memory itself …
resistive random access memories (RRAMs) to process the data within the memory itself …
Computational cxl-memory solution for accelerating memory-intensive applications
J Sim, S Ahn, T Ahn, S Lee, M Rhee… - IEEE Computer …, 2022 - ieeexplore.ieee.org
CXL interface is the up-to-date technology that enables effective memory expansion by
providing a memory-sharing protocol in configuring heterogeneous devices. However, its …
providing a memory-sharing protocol in configuring heterogeneous devices. However, its …
In-memory computing for machine learning and deep learning
In-memory computing (IMC) aims at executing numerical operations via physical processes,
such as current summation and charge collection, thus accelerating common computing …
such as current summation and charge collection, thus accelerating common computing …
Severity-based hierarchical ECG classification using neural networks
S Diware, S Dash, A Gebregiorgis… - … Circuits and Systems, 2023 - ieeexplore.ieee.org
Timely detection of cardiac arrhythmia characterized by abnormal heartbeats can help in the
early diagnosis and treatment of cardiovascular diseases. Wearable healthcare devices …
early diagnosis and treatment of cardiovascular diseases. Wearable healthcare devices …
Efficient pipelined execution of CNNs based on in-memory computing and graph homomorphism verification
In-memory computing is an emerging computing paradigm enabling deep-learning
inference at significantly higher energy-efficiency and reduced latency. The essential idea is …
inference at significantly higher energy-efficiency and reduced latency. The essential idea is …
High-throughput, area-efficient, and variation-tolerant 3-D in-memory compute system for deep convolutional neural networks
Untethered computing using deep convolutional neural networks (DCNNs) at the edge of
IoT with limited resources requires systems that are exceedingly power and area-efficient …
IoT with limited resources requires systems that are exceedingly power and area-efficient …
An energy-efficient sparse deep-neural-network learning accelerator with fine-grained mixed precision of FP8–FP16
Recently, several hardware have been reported for deep-neural-network (DNN)
acceleration, however, they focused on only inference rather than DNN learning that is …
acceleration, however, they focused on only inference rather than DNN learning that is …
Training hardware for binarized convolutional neural network based on CMOS invertible logic
In this article, we implement fast and power-efficient training hardware for convolutional
neural networks (CNNs) based on CMOS invertible logic. The backpropagation algorithm is …
neural networks (CNNs) based on CMOS invertible logic. The backpropagation algorithm is …