A Reconfigurable Multiplier/Dot-Product Unit for Precision-Scalable Deep Learning Applications

Across different Deep Learning (DL) applications or within the same application but in different phases, bitwidth precision of activations and weights may vary. Moreover, energy and latency of MAC units have to be minimized, especially at the edge. Hence, various precision-scalable MAC units optimized for DL have recently emerged. Our contribution is a new precision-configurable multiplier/dot-product unit based on a modified Radix-4 Booth signed multiplier with Sum-Together (ST) mode. Besides 16-bit full precision multiplications, it can be reconfigured to perform dot products among two 8-bit or four 4-bit sub words of the input operands without requiring an external adder, thus reducing the number of cycles of MAC operations. The results of the synthesis in performance, power and area on a 28-nm technology show that our unit (1) is superior to other state of the art ST multipliers in area (≈35% less) in the clock frequency range between 100 and 1000 MHz and (2) reduces latency up to 4x when used to compute a convolutional layer, at the cost of limited overheads in area (+10%) and power (+13%) compared to a conventional 16-bit Booth multiplier. This unit can play an important role in designing variable-precision MAC units or DL accelerators for edge devices.