29 7.8 0.12 A5 259 three.9 0.12 A6 246 four.1 0.13 A7 492 2.0 0.13 A8 140 7.1 0.Future Internet 2021, 13,16 of120 A1 – (13,8)Quantity of
29 7.8 0.12 A5 259 three.9 0.12 A6 246 four.1 0.13 A7 492 2.0 0.13 A8 140 7.1 0.Future World-wide-web 2021, 13,16 of120 A1 – (13,eight)Number of Cores60 A8 – (13,four) 40 A6 – (4,8) A3 – (13,2) 20 A7 – (four,4)A4 – (eight,eight);A2 – (13,4)A5 – (eight,four)0,2,four,6,0 eight,0 ten,0 Frames per Second (FPS)12,14,16,Figure 9. The number of cores versus frames per second of every single configuration of the architecture. The graphs indicate the configuration as number of lines of cores and number of columns of cores).Table 9 presents the Tiny-YOLOv3 network execution times on many platforms: Intel i7-8700 @ 3.two GHz, GPU RTX 2080ti, and embedded GPU Jetson TX2 and Jetson Nano. The CPU and GPU outcomes had been obtained employing the original Tiny-YOLOv3 network [42] with floating-point representation. The CPU outcome corresponds to the execution of Tiny-YOLOv3 implemented in C. The GPU outcome was obtained from the execution of Tiny-YOLOv3 within the Pytorch environment applying CUDA libraries.Table 9. Tiny-YOLOv3 execution occasions on a number of platforms. Software Version Floating-point Floating-point Floating-point Floating-point Fixed-point-16 Fixed-point-8 Platform CPU (Intel i7-8700 @ three.two GHz) GPU (RTX 2080ti) eGPU (Jetson TX2) [43] eGPU (Jetson Nano) [43] ZYNQ7020 ZYNQ7020 CNN (ms) 819.two 7.five 140 68 FPS 1.2 65.0 17 1.2 7.1 14.The Tiny-YOLOv3 on desktop CPUs is as well slow. The inference time on an RTX 2080ti GPU showed a 109 speedup versus the desktop CPU. Making use of the proposed accelerator, the inference occasions have been 140 and 68 ms, in the ZYNQ7020. The low-cost FPGA was 6X (16-bit) and 12X (8-bit) faster than the CPU using a small drop in accuracy of 1.4 and 2.1 points, respectively. In comparison to the embedded GPU, the proposed architecture was 15 slower. The advantage of utilizing the FPGA could be the energy consumption. Jetson TX2 includes a energy close to 15 W, whilst the proposed accelerator has a energy of around 0.5 W. The Nvidia Jetson Nano consumes a maximum of ten W but is about 12DMPO MedChemExpress slower than the proposed architecture. 5.3. Comparison with Other FPGA Implementations The proposed implementation was compared with preceding accelerators of TinyYOLOv3. We report the quantization, the operating frequency, the occupation of FPGA sources (DSP, LUTs, and BRAMs), and two overall performance metrics (execution time and frames per second). Moreover, we thought of three metrics to quantify how efficientlyFuture Online 2021, 13,17 ofthe hardware resources have been being applied. Because distinct solutions commonly have a unique number of sources, it can be fair to think about metrics to somehow normalize the outcomes prior to comparison. FSP/kLUT, FPS/DSP, and FPS/BRAM establish the amount of each and every resource that may be employed to Sutezolid Purity & Documentation create a frame per second. The larger these values, the larger the utilization efficiency of those sources (see Table ten).Table 10. Functionality comparison with other FPGA implementations. [38] Device Dataset Quant. Freq. (MHz) DSPs LUTs BRAMs Exec. (ms) FPS FPS/kLUT FPS/DSP FPS/BRAM ZYNQZU9EG Pedestrian indicators 8 9.6 104 16 100 120 26 K 93 532.0 1.9 0.07 0.016 0.020 18 200 2304 49 K 70 [39] ZYNQ7020 [41] [40] Ours ZYNQVirtexVX485T US XCKU040 COCO dataset 16 143 832 139 K 384 24.four 32 0.23 0.038 0.16 100 208 27.5 K 120 140 7.1 0.26 0.034 0.eight one hundred 208 33.4 K 120 68 14.7 0.44 0.068 0.The implementation in [39] may be the only earlier implementation having a Zynq 7020 SoC FPGA. This device has substantially fewer resources than the devices applied within the other performs. Our architecture implemented inside the identical device was 3.7X and 7.4X more rapidly, rely.
