16 citations · 28 across the 5 of their papers we have counts for
5 papers
Memristive tabular variational autoencoder for compression of analog data in high energy physics
Rajat Gupta, Yuvaraj Elangovan, Tae Min Hong +5
We present an implementation of edge AI to compress data on an in-memory analog content-addressable memory (ACAM) device. A variational autoencoder is trained on a simulated sample…
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…
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…
RACE-IT: A Reconfigurable Analog Computing Engine for In-Memory Transformer Acceleration
Lei Zhao, Aishwarya Natarajan, Luca Buonanno +6
Transformer models represent the cutting edge of Deep Neural Networks (DNNs) and excel in a wide range of machine learning tasks. However, processing these models demands significa…
X-TIME: An in-memory engine for accelerating machine learning on tabular data with CAMs
Giacomo Pedretti, John Moon, Pedro Bruel +11
Structured, or tabular, data is the most common format in data science. While deep learning models have proven formidable in learning from unstructured data such as images or speec…