ConSmax: Hardware-Friendly Alternative Softmax with Learnable Parameters

no code implementations • 31 Jan 2024 • Shiwei Liu, Guanchen Tao, Yifei Zou, Derek Chow, Zichen Fan, Kauna Lei, Bangfei Pan, Dennis Sylvester, Gregory Kielian, Mehdi Saligane

Compared to state-of-the-art Softmax hardware, ConSmax results in 14. 5x energy and 14. 0x area savings with a comparable accuracy on a GPT-2 model and the WikiText103 dataset.

Language Modelling Large Language Model

Millimeter-Scale Ultra-Low-Power Imaging System for Intelligent Edge Monitoring

no code implementations • 9 Mar 2022 • Andrea Bejarano-Carbo, Hyochan An, Kyojin Choo, Shiyu Liu, Qirui Zhang, Dennis Sylvester, David Blaauw, Hun-Seok Kim

Millimeter-scale embedded sensing systems have unique advantages over larger devices as they are able to capture, analyze, store, and transmit data at the source while being unobtrusive and covert.

Data Compression Event Detection +1

Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks

no code implementations • 9 May 2018 • Charles Eckert, Xiaowei Wang, Jingcheng Wang, Arun Subramaniyan, Ravi Iyer, Dennis Sylvester, David Blaauw, Reetuparna Das

This paper presents the Neural Cache architecture, which re-purposes cache structures to transform them into massively parallel compute units capable of running inferences for Deep Neural Networks.

Quantization