DocumentCode :
1749053
Title :
Massively parallel inner-product array processor
Author :
Genov, Roman ; Cauwenberghs, Gert
Author_Institution :
Dept. of Electr. & Comput. Eng., Johns Hopkins Univ., Baltimore, MD, USA
Volume :
1
fYear :
2001
fDate :
2001
Firstpage :
183
Abstract :
We present a hardware architecture for parallel inner-product array computation in very high dimensional feature spaces, towards a general-purpose kernel-based classifier and function approximator. The architecture is internally analog with fully digital interface. On-chip analog fine-grain parallel processing yields real-time throughput levels for high-dimensional classification tasks. The architecture contains an array of computational cells with integrated digital storage and a parallel bank of A/D converters. Digital multiplication with enhanced resolution is obtained with bit-serial input vectors and bit-parallel storage of weights, by combining quantized outputs from multiple rows of binary unit cells over time. A prototype 128×512 inner-product array processor on a single 3 mm ×3 mm chip fabricated in standard CMOS 0.5 μm technology achieves 8-bit effective resolution. An efficient real-time massively-parallel hardware architecture of a support vector machine classifier is presented
Keywords :
CMOS integrated circuits; function approximation; mixed analogue-digital integrated circuits; neural chips; neural net architecture; parallel architectures; pattern classification; CMOS chip; analog fine-grain processing; digital interface; function approximation; inner-product array; massively parallel processor; parallel processing; pattern classification; support vector machine; CMOS process; CMOS technology; Computer architecture; Concurrent computing; Hardware; Parallel processing; Prototypes; Support vector machine classification; Support vector machines; Throughput;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
ISSN :
1098-7576
Print_ISBN :
0-7803-7044-9
Type :
conf
DOI :
10.1109/IJCNN.2001.939014
Filename :
939014
Link To Document :
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