Work

Accelerating applications using edge tensor processing units

Year: 2021

Type: conference-paper

Abstract: Neural network (NN) accelerators have been integrated into a wide-spectrum of computer systems to accommodate the rapidly growing demands for artificial intelligence (AI) and machine learning (ML) applications. NN accelerators share the idea of providing native hardware support for operations on multidimensional tensor data. Therefore, NN accelerators are theoretically tensor processors that can improve system performance for any problem that uses tensors as inputs/outputs. Unfortunately, commercially available NN accelerators only expose computation capabilities through AI/ML-specific interfaces. Furthermore, NN accelerators reveal very few hardware design details, so applications cannot easily leverage the tensor operations NN accelerators provide.

Source: International Conference for High Performance Computing, Networking, Storage and Analysis (SC)

DOI: 10.1145/3458817.3476177

Authors: Kuan-Chieh Hsu, Hung-Wei Tseng

Institution: University of California System

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Science Foundation

Award: CNS Core: Small: Re-engineering Applications for Tensor Processing Units