Abstract
The super power of deep learning in image classification problems have become very popular and applicable in many areas like medical sciences. Some of the medical applications are real-time and may be implemented in embedded devices. In these cases, achieving the highest level of accuracy is not the only concern. Computation runtime and power consumption are also considered as the most important performance indicators. These parameters are mainly evaluated in hardware design phase. In this research, an energy efficient deep learning accelerator for endoscopic images classification (DLA-E) is proposed. This accelerator can be implemented in the future endoscopic imaging equipments for helping medical specialists during endoscopy or colonoscopy in order of making faster and more accurate decisions. The proposed DLA-E consists of 256 processing elements with 1000 bps network on chip bandwidth. Based on the simulation results of this research, the best dataflow for this accelerator based on MobileNet v2 is kcp_ws from the weight stationary (WS) family. Total energy consumption and total runtime of this accelerator on the investigated dataset is 4.56 × 109 MAC (multiplier–accumulator) energy and 1.73 × 107 cycles respectively, which is the best result in comparison to other combinations of CNNs and dataflows.
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