From the 1 of 6 linked papers with an AI index.
6 papers
NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference
Jiajun Hu, Ruthwik Reddy Sunketa, Lei Zhao +3
The paper proposes a new FPGA architecture that replaces ADCs with analog content‑addressable memories to enable ADC‑free in‑memory computing, allowing both linear and nonlinear op…
A Fast and Energy-Efficient Latch-Based Memristive Analog Content-Addressable Memory
Paul-Philipp Manea, Aishwarya Natarajan, Jim Ignowski +2
Analog content-addressable memories (aCAMs) based on memristors provide a promising pathway toward energy-efficient large-scale associative computing for Edge AI and embedded intel…
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…
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…