10T SRAM Computing-in-Memory Macros for Binary and Multibit MAC Operation of DNN Edge Processors

نویسندگان

چکیده

Computing-in-memory (CIM) is a promising approach to reduce latency and improve the energy efficiency of multiply-and-accumulate (MAC) operation under memory wall constraint for artificial intelligence (AI) edge processors. This paper proposes an focusing on scalable CIM designs using new ten-transistor (10T) static random access (SRAM) bit-cell. Using proposed 10T SRAM bit-cell, we present two SRAM-based (SRAM-CIM) macros supporting multibit binary MAC operations. The first design achieves fully parallel computing high throughput 32 Advanced circuit techniques such as input-dependent dynamic reference generator input-boosted sense amplifier are presented. Fabricated in 28 nm CMOS process, this 409.6 GOPS throughput, 1001.7 TOPS/W efficiency, 169.9 TOPS/mm 2 area efficiency. effectively solves previous problems writing disturb, power consumption analog digital converter (ADC). second supports (4-b weight, 4-b input, 8-b output) increase inference accuracy. We propose architecture that divides weight input multiplication four 2-b parallel, which increases signal margin by 16× compared conventional multiplication. Besides, capacitive digital-to-analog (CDAC) issue addressed intrinsic bit-line capacitance existing SRAM-CIM architecture. realizing CDAC successfully demonstrated with modified LeNet-5 neural network. These results demonstrate bit-cell robust designs, essential computing.

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ژورنال

عنوان ژورنال: IEEE Access

سال: 2021

ISSN: ['2169-3536']

DOI: https://doi.org/10.1109/access.2021.3079425