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Table 3 A comparison between different aspects related to the devices

From: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Feature

Assessment

Leader

Development

CPU is the easiest to program, then GPU, then FPGA

CPU

Size

Both FPGA and CPU have smaller volume solutions due to their lower power consumption

FPGA-CPU

Customization

Broader flexibility is provided by FPGA

FPGA

Ease of change

Easier way to vary application functionality is provided by GPU and CPU

GPU-CPU

Backward compatibility

Transferring RTL to novel FPGA requires additional work. Furthermore, GPU has less stable architecture than CPU

CPU

Interfaces

Several varieties of interfaces can be implemented using FPGA

FPGA

Processing/$

FPGA configurability assists utilization in wider acceleration space. Due to the considerable processing abilities, GPU wins

FPGA-GPU

Processing/watt

Customized designs can be optimized

FPGA

Timing latency

Implemented FPGA algorithm offers deterministic timing, which is in turn much faster than GPU

FPGA

Large data analysis

FPGA performs well for inline processing, while CPU supports storage capabilities and largest memory

FPGA-CPU

DCNN inference

FPGA has lower latency and can be customized

FPGA

DCNN training

Greater float-point capabilities provided by GPU

GPU