Designing Deep Learning Hardware Accelerator and Efficiency Evaluation

Area of Science:

• Computer Science
• Electrical Engineering
• Artificial Intelligence

Background:

• Deep learning applications, particularly convolutional neural networks (CNNs), demand significant computational resources, challenging traditional processors.
• There is an urgent need for efficient and low-energy solutions to meet the growing demands of CNN computations.
• Field-programmable gate arrays (FPGAs) offer potential advantages like high parallelism, low power consumption, and programmability for CNN acceleration.

Purpose of the Study:

• To review state-of-the-art FPGA-based accelerator designs for CNNs, highlighting their contributions and limitations.
• To explore the concepts of parallel computing (PC) within convolution algorithms and their implementation on FPGA hardware.
• To evaluate the performance and energy efficiency of a proposed CPU+FPGA framework compared to traditional computation methods.

Main Methods:

• Literature review of existing FPGA-based CNN accelerator designs.
• Analysis of parallel computing strategies applicable to convolution algorithms on FPGAs.
• Implementation and experimental evaluation of a hybrid CPU+FPGA computational framework.

Main Results:

• FPGA platforms demonstrate superior operational efficiency compared to traditional central processing units (CPUs) and graphics processing units (GPUs).
• The proposed CPU+FPGA framework achieved significant improvements in performance and energy consumption ratio.
• Analysis confirmed the high parallelism achievable for convolution algorithms on FPGA hardware structures.

Conclusions:

• FPGA-based accelerators are a promising strategy for addressing the computational challenges posed by deep learning, specifically CNNs.
• The explored parallel computing techniques on FPGAs effectively enhance CNN computation efficiency.
• The CPU+FPGA framework offers a compelling solution for high-performance, energy-efficient deep learning acceleration.