Abstract:
Multiply-accumulate (MAC) operations are common in data processing and machine learning but costly in terms of hardware usage. Stochastic Computing (SC) is a promising ap...Show MoreMetadata
Abstract:
Multiply-accumulate (MAC) operations are common in data processing and machine learning but costly in terms of hardware usage. Stochastic Computing (SC) is a promising approach for low-cost hardware design of complex arithmetic operations such as multiplication. Computing with deterministic unary bit-streams (defined as bit-streams with all 1s grouped at the beginning or end of a bit-stream) has been recently suggested to improve the accuracy of SC. Conventionally, SC designs use multiplexer (mux) units or OR gates to accumulate data in the stochastic domain. MUX-based addition suffers from scaling of data and OR-based addition from inaccuracy. This work proposes a novel technique for MAC operation on unary bit-streams that allows exact, non-scaled addition of multiplication results. By introducing a relative delay between the products, we control correlation between bit-streams and eliminate OR-based addition error. We evaluate the accuracy of the proposed technique compared to the state-of-the-art MAC designs. After quantization, the proposed technique demonstrates at least 37 percent and up to 100 percent decrease of the mean absolute error for uniformly distributed random input values, compared to traditional OR-based MAC designs. Further, we demonstrate that the proposed technique is practical and evaluate area, power and energy of three possible implementations.
Published in: IEEE Transactions on Computers ( Volume: 71, Issue: 6, 01 June 2022)