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A highly scalable 3D chip for binary neural network classification applications

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1 Author(s)
Bermak, A. ; Dept. of Electr. & Electron. Eng., Hong Kong Univ. of Sci. & Technol., Kowloon, China

This paper describes a 3D VLSI Chip for binary neural network classification applications. The 3D circuit includes three layers of MCM integrating 4 chips each making it a total of 12 chips integrated in a volume of (2 × 2 × 0.7)cm3. The architecture is scalable, and real-time binary neural network classifier systems could be built with one, two or all twelve chip solutions. Each basic chip includes an on-chip control unit for programming options of the neural network topology and precision. The system is modular and presents easy expansibility without requiring extra devices. Experimental test results showed that a full recall operation is obtained in less than 1.2μs for any topology with 4-bit or 8-bit precision while it is obtained in less than 2.2μs for any 16-bit precision. As a consequence the 3D chip is a very powerful reconfigurable and a multiprecision neural chip exhibiting a significant speed of 1.25 GCPS.

Published in:

Circuits and Systems, 2003. ISCAS '03. Proceedings of the 2003 International Symposium on  (Volume:5 )

Date of Conference:

25-28 May 2003

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