Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/172973
Title: Broad-Purpose In-Memory Computing for Signal Monitoring and Machine Learning Workloads Based on Commercial Bitcell
Authors: SAURABH JAIN 
LIN LONGYANG 
ALIOTO,MASSIMO BRUNO 
Issue Date: 28-Jul-2020
Citation: SAURABH JAIN, LIN LONGYANG, ALIOTO,MASSIMO BRUNO (2020-07-28). Broad-Purpose In-Memory Computing for Signal Monitoring and Machine Learning Workloads Based on Commercial Bitcell. IEEE ASSCC 2020. ScholarBank@NUS Repository.
Abstract: In this paper, a broad-purpose compute-in-memory solution (±CIM) able to handle arbitrary sign in both inputs/features and weights/coefficients is introduced. The ability to operate on arbitrary sign and under variable precision on both operands enables a wide range of applications, ranging from conventional neural networks to digital signal processing and monitoring. The ±CIM pipelined architecture, the reconfigurable row encoder and the adoption of a commercial 2-port bitcell allow uninterrupted memory availability for conventional read/write, even when performing in-memory computations. A 40nm testchip shows the ability of the ±CIM architecture to perform both neural network computations and classical signal processing. At 6-bit precision, the measured worst-case mismatch (noise) is 0.38 (0.62) LSB. The achieved accuracy when executing a LeNet-5 neural net workload is 98.3%, which is within 1.3% of state-of-the-art software implementations. As example of signal processing workload, 91.7% accuracy is achieved in voice activity detection, which is within 2.8% of a software implementation. Overall, the energy efficiency (throughput) of 41 TOPS/W (122 GOPS) is achieved at 38% area overhead, over a conventional SRAM with the same 4-KB capacity.
Source Title: IEEE ASSCC 2020
URI: https://scholarbank.nus.edu.sg/handle/10635/172973
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