A Twin-8T SRAM Computation-in-Memory Unit-Macro for Multibit CNN-Based AI Edge Processors
Computation-in-memory (CIM) is a promising candidate to improve the energy efficiency of multiply-and-accumulate (MAC) operations of artificial intelligence (AI) chips. This work presents an static random access memory (SRAM) CIM unit-macro using: 1) compact-rule compatible twin-8T (T8T) cells for w...
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Published in | IEEE journal of solid-state circuits Vol. 55; no. 1; pp. 189 - 202 |
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Main Authors | , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | Computation-in-memory (CIM) is a promising candidate to improve the energy efficiency of multiply-and-accumulate (MAC) operations of artificial intelligence (AI) chips. This work presents an static random access memory (SRAM) CIM unit-macro using: 1) compact-rule compatible twin-8T (T8T) cells for weighted CIM MAC operations to reduce area overhead and vulnerability to process variation; 2) an even-odd dual-channel (EODC) input mapping scheme to extend input bandwidth; 3) a two's complement weight mapping (C2WM) scheme to enable MAC operations using positive and negative weights within a cell array in order to reduce area overhead and computational latency; and 4) a configurable global-local reference voltage generation (CGLRVG) scheme for kernels of various sizes and bit precision. A 64 <inline-formula> <tex-math notation="LaTeX">\times </tex-math></inline-formula> 60 b T8T unit-macro with 1-, 2-, 4-b inputs, 1-, 2-, 5-b weights, and up to 7-b MAC-value (MACV) outputs was fabricated as a test chip using a foundry 55-nm process. The proposed SRAM-CIM unit-macro achieved access times of 5 ns and energy efficiency of 37.5-45.36 TOPS/W under 5-b MACV output. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
ISSN: | 0018-9200 1558-173X |
DOI: | 10.1109/JSSC.2019.2952773 |