XNOR-SRAM: In-Memory Computing SRAM Macro for Binary/Ternary Deep Neural Networks

We present XNOR-SRAM, a mixed-signal in-memory computing (IMC) SRAM macro that computes ternary-XNOR-and-accumulate (XAC) operations in binary/ternary deep neural networks (DNNs) without row-by-row data access. The XNOR-SRAM bitcell embeds circuits for ternary XNOR operations, which are accumulated...

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Veröffentlicht in:	IEEE journal of solid-state circuits Jg. 55; H. 6; S. 1733 - 1743
Hauptverfasser:	Yin, Shihui, Jiang, Zhewei, Seo, Jae-Sun, Seok, Mingoo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.06.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Analog to digital conversion Analog to digital converters Artificial neural networks Binary weights CMOS Complexity theory Computation Computer architecture deep neural networks (DNNs) Energy conversion efficiency Energy efficiency ensemble learning Hardware in-memory computing (IMC) Neural networks Random access memory SRAM Static random access memory System-on-chip ternary activations Transistors Voltage dividers
ISSN:	0018-9200, 1558-173X
Online-Zugang:	Volltext
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