6 papers
NL-DPE: An Analog In-memory Non-Linear Dot Product Engine for Efficient CNN and LLM Inference
Lei Zhao, Luca Buonanno, Archit Gajjar +9
Resistive Random Access Memory (RRAM) based in-memory computing (IMC) accelerators offer significant performance and energy advantages for deep neural networks (DNNs), but face thr…
Fault-Free Analog Computing with Imperfect Hardware
Zhicheng Xu, Jiawei Liu, Sitao Huang +9
The growing demand for edge computing and AI drives research into analog in-memory computing using memristors, which overcome data movement bottlenecks by computing directly within…
Hardware-Adaptive and Superlinear-Capacity Memristor-based Associative Memory
Chengping He, Mingrui Jiang, Keyi Shan +6
Brain-inspired computing aims to mimic cognitive functions like associative memory, the ability to recall complete patterns from partial cues. Memristor technology offers promising…
Real-time raw signal genomic analysis using fully integrated memristor hardware
Peiyi He, Shengbo Wang, Ruibin Mao +6
Advances in third-generation sequencing have enabled portable and real-time genomic sequencing, but real-time data processing remains a bottleneck, hampering on-site genomic analys…
Solving Boolean satisfiability problems with resistive content addressable memories
Giacomo Pedretti, Fabian Böhm, Tinish Bhattacharya +15
Solving optimization problems is a highly demanding workload requiring high-performance computing systems. Optimization solvers are usually difficult to parallelize in conventional…
Efficient Nonlinear Function Approximation in Analog Resistive Crossbars for Recurrent Neural Networks
Junyi Yang, Ruibin Mao, Mingrui Jiang +9
Analog In-memory Computing (IMC) has demonstrated energy-efficient and low latency implementation of convolution and fully-connected layers in deep neural networks (DNN) by using p…