Design and implementation of high-speed and energy-efficient variable-latency speculating booth multiplier (VLSBM)

Data hazards cause severe pipeline performance degradation for data-intensive computing processes. To improve the performance under a pessimistic assumption on the pipeline efficiency, a high-speed and energy-efficient VLSBM is proposed that successively performs a speculating and correcting phase. To reduce the critical path, the VLSBM partial products are partitioned into the (n-z)-bit least significant part (LSP) and the self-reliant (n+z)-bit most significant part (MSP), and an estimation function stochastically predicts the carry to the MSP, thereby allowing independent calculation of the partial-product accumulation of parts. When a carry prediction is accurate, the data dependence is hidden and the correcting phase is bypassed, thereby ensuring the potential speed-up of the pipelined datapath. If a prediction is inaccurate, the speculation is flushed and the correcting phase is executed to obtain the exact multiplication. The simulation results verify the effectiveness of the proposed VLSBM. When applied to a DSP algorithm with a data hazard (or dependence) probability P _D , 0\leq P-{D}\leq 1$, the results show that the proposed VLSBM outperforms the original Booth multiplier and the fastest conventional well-pipelined modified Booth multiplier when P-{D}>0.32. For the case of high $P _D with P-{D}\approx 1, the proposed VLSBM improves approximately 1.47 times speedup against the fastest conventional pipelined Booth multiplier (@UMC 90 nm CMOS) and, furthermore, approximately 25.4% of energy per multiplication and 7% of area are saved. By examining multiplications during three multimedia application processes (i.e., JPEG compression, object detection, and H.264/AVC decoding), the proposed VLSBM improves the speed-up ratio by approximately 1.0 to 1.4 times, and reduces the cycle count ratio by approximately 1.3 to 1.8 times in comparison to the fastest conventional two-stage pipelined Booth multiplier.