IEEE

20.5 C-Transformer: A 2.6-18.1μJ/Token Homogeneous DNN-Transformer/Spiking-Transformer Processor with Big-Little Network and Implicit Weight Generation for Large Language Models

Abstract: Recently, transformer-based large language models (LLMs), shown in Fig. 20.5.1, are widely used, and even on-device LLM systems with real-time responses are anticipated [1]. Many transformer ...
IEEE

C-DNN: A 24.5-85.8TOPS/W Complementary-Deep-Neural-Network Processor with Heterogeneous CNN/SNN Core Architecture and Forward-Gradient-Based Sparsity Generation

Abstract: Spiking-Neural-Networks (SNNs) have been studied for a long time, and recently have been shown to achieve the same accuracy as Convolutional-Neural-Networks (CNNs). By using CNN-to-SNN ...
Frontiers

A Practical Guide to the Numerical Implementation of Tensor Networks I: Contractions, Decompositions, and Gauge Freedom

School of Physics, Georgia Institute of Technology, Atlanta, GA, United States We present an overview of the key ideas and skills necessary to begin implementing tensor network methods numerically, ...