Close

Presentation

CIM-BLAS: Computing-in-Memory Accelerator for BLAS
SessionNeed a Break from AI? Memory-centric Computing for Beyond Machine Learning Application
DescriptionThe Basic Linear Algebra Subprograms (BLAS) is a fundamental software library. Many operations in BLAS are data-intensive and are limited by the memory bandwidth of the CPU and GPU. The computing-in-memory (CIM) technology can effectively alleviate the memory wall bottleneck and is particularly suitable for accelerating BLAS. We propose the first CIM accelerator for BLAS, CIM-BLAS, based on non-volatile memory. CIM-BLAS includes a unified floating-point pipeline to support high-precision arithmetics. High efficiency of the accelerator is achieved by developing configurable data flows to support various BLAS functions. The evaluations demonstrate the significant potential of CIM for accelerating BLAS.
Authors
Rui Liu
Xiangtan University
Zerun Li
Institute of Computing Technology, Chinese Academy of Sciences
Xiaoyu Zhang
Institute of Computing Technology, Chinese Academy of Sciences
Xiaoming Chen
Institute of Computing Technology, Chinese Academy of Sciences
yinhe han
Institute of Computing Technology, Chinese Academy of Sciences
Minghua Tang
Xiangtan University
Event Type
Research Manuscript
TimeWednesday, June 252:00pm - 2:15pm PDT
Location3002, Level 3
Next PresentationNext Presentation
Segmented Angular Pre-Processing for Accurate and Efficient In-Memory Vector Similarity Search
Similar Presentations
VQT-CiM: Accelerating Vector Quantization Enhanced Transfomer with Ferroelectric Compute-in-Memory
CREST-CiM: Cross-Coupling-Enhanced Differential STT-MRAM for Robust Computing-in-Memory in Binary Neural Networks
An Efficient Compute-in-Memory based Accelerator for Point-based Point Cloud Neural Networks