A 345mW heterogeneous many-core processor with an intelligent inference engine for robust object recognition

Author

Seungjin Lee ; Jinwook Oh ; Minsu Kim ; Junyoung Park ; Joonsoo Kwon ; Hoi-Jun Yoo

Author_Institution

KAIST, Daejeon, South Korea

fYear

2010

fDate

7-11 Feb. 2010

Firstpage

332

Lastpage

333

Abstract

A 228GOPS 345 mW heterogeneous many-core processor combines bottom-up and top-down attention for high accuracy. It uses analog-digital mixed-mode neuro-fuzzy inference circuits to realize robust object recognition. Weight perturbation learning and workload-aware voltage/frequency control are adopted. The 5 Ã— 10 mm² chip with 96% recognition accuracy contains 4 SIMD vector processing elements and 32 MIMD processors.

Keywords

frequency control; fuzzy reasoning; multiprocessing systems; neural nets; object recognition; perturbation techniques; voltage control; 32 MIMD processor; 4 SIMD vector processing element; analog-digital mixed mode neuro fuzzy inference circuits; heterogeneous many core processor; intelligent inference engine; power 3.5 mW; robust object recognition; weight perturbation learning; workload aware voltage-frequency control; Acceleration; Computer vision; Energy consumption; Engines; Forward error correction; Layout; Neurofeedback; Object recognition; Robustness; Throughput;

fLanguage

English

Publisher

ieee

Conference_Titel

Solid-State Circuits Conference Digest of Technical Papers (ISSCC), 2010 IEEE International

Conference_Location

San Francisco, CA

ISSN

0193-6530

Print_ISBN

978-1-4244-6033-5

Type

conf

DOI

10.1109/ISSCC.2010.5433905

Filename

5433905