Multiply–accumulate (MAC) computations account for a large part of machine learning accelerator operations. The pipelined structure is usually adopted to improve the performance by reducing the length of critical paths. An increase in the number of flip-flops due to pipelining, however, generally results in significant area and power increase. A large number of flip-flops are often required to meet the feed forward-cutset rule. Based on the observation that this rule can be relaxed in machine learning applications, we propose a pipelining method that eliminates some of the flip-flops selectively. The simulation results show that the proposed MAC unit achieved a 20% energy saving and a 20% area reduction compared with the conventional pipelined MAC.
Feed Forward Cutset Free technique decreases the Pipeline stages.
Less area and shorter critical path delay when using the concept of DADDA multiplier.