He convolution operation, as shown in Figure 10B. Then we study the following m sets

He convolution operation, as shown in Figure 10B. Then we study the following m sets of ifmap, and repeat the steps in Figure ten until the complete ifmap comprehensive the convolution operation. For layers with huge ifmap aspect ratio, we are going to adopt the ifmap reuse technique to replace convolutional reuse technique.Micromachines 2021, 12,n sets of GMP-grade Proteins Synonyms filter in order within the vertical direction, for instance in Figure 9A. Following each and every round of convolution operation is completed, the n sets of filter will not be replaced but replace the following batch of m sets of ifmap. This replacing process continues until the entire ifmap of this layer full convolution operation, as shown in Figure 9B. Then we read the subsequent n sets of filter, and repeat the steps in Figure 9 until all r sets of filter full the convo10 of 18 lution operation. For layers with massive filter aspect ratio, we will adopt the filter reuse approach to replace convolutional reuse approach.Micromachines 2021, 12, x FOR PEER REVIEW11 ofFigure 9. Process of filter reuse (A) the initial iteration (B) the successive iterations. Figure 9. Procedure of filter reuse (A) the very first iteration (B) the successive iterations.In contrast to filter reuse method, Figure 10 illustrates the ifmap reuse approach. We read m sets of ifmap in order in the horizontal direction, and study n sets of filter in order in the vertical direction, including in Figure 10A. Right after each and every round of convolution operation is completed, the m sets of ifmap usually are not replaced but replace the subsequent batch of n sets of filter. This filter replacing procedure continues till the all r sets of filter of this layer full the convolution operation, as shown in Figure 10B. Then we read the following m sets of ifmap, and repeat the methods in Figure 10 until the complete ifmap full the convolution operation. For layers with big ifmap aspect ratio, we’ll adopt the ifmap reuse technique to replace convolutional reuse technique.Figure ten. Process of ifmap reuse (A) the initial iteration (B) the successive iterations. Figure ten. Process of ifmap reuse (A) the very first iteration (B) the successive iterations.four. Experiment Outcomes four. Experiment Outcomes We modify SCALE-Sim [33] to evaluate our methodology on HarDNet39 [32] and We modify SCALE-Sim [33] to evaluate our methodology on HarDNet39 [32] and DenseNet121 [34]. Table 1 shows the 4 target architecture configurations in the fixed DenseNet121 [34]. proposed Mirogabalin besylate Purity & Documentation reconfigurable procedures. Within the fixed dataflow, all configuradataflow and our Table 1 shows the 4 target architecture configurations on the fixed dataflow and our all layers, reconfigurable procedures. Inside the fixed dataflow, all configurations are fixed in proposed size of PE array are 16 16 and 32 32, respectively; input tions are fixed inbuffer are equally PE array are total buffer size re 128 KB and 256 KB, buffer and filter all layers, size of partitioned, 16 16 and 32 32, respectively; input buffer and filter buffer is fixed to output stationary with convolutional reuse. WhileKB, respectively; dataflow are equally partitioned, total buffer size are 128 KB and 256 for respectively; dataflow is fixed toand dataflow are reconfigurable layer by layerWhile for our methodologies, architecture output stationary with convolutional reuse. primarily based on our methodologies, architecture total dataflow are reconfigurable layer by layer also fixed the given total PE number and and buffer size of input and filter, dataflow is primarily based on theoutput total PE quantity an.