Using function folding to improve silicon efficiency of reconfigurable arithmetic arrays

This work presents function folding, a design principle to improve the silicon efficiency of reconfigurable arithmetic (coarse-grain) arrays. Though highly parallel implementations of DSP algorithms have been demonstrated on these arrays, the overall silicon efficiency of current devices is limited by both the large numbers of ALUs required in the array and by the only moderate speeds which are achieved. The operating frequencies are mainly limited by the requirements of nonlocal routing connections. We present a novel approach to overcome these limitations: In function folding, a small number of distinct operators belonging to the same configuration are folded onto the same ALU, i.e. executed sequentially on one processing element. The ALU is controlled by a program repetitively executing the same instruction sequence. Data only required locally is stored in a local register file. This sequential approach uses the individual ALU resources more efficiently, while all processing elements of the array work in parallel as in current devices. Additionally, the ALUs and local registers can be clocked with a higher frequency than the (nonlocal) routing connections. Overall, a higher computational density than in current devices results.