OpenNPU: an open source platform for automatic neural network synthesis for FPGAs.

• Ano de defesa: 2023
• Autor(a) principal: Muyal, Thomas Araújo
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Artificial intelligence; Circuitos integrados VLSI; Deep learning; Eletrônica digital; FPGA; Hardware accelerator; Inteligência artificial; Internet das coisas; Internet of things; Machine learning; Neural networks; Redes neurais
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/3/3142/tde-26042023-153703/

Artificial neural networks are a group of artificial intelligence algorithms widely used contemporarily in the industry and research, implemented to solve a great variety of issues. Its recent popularity is in part due to the advance of hardware technologies, which provide computational resources for the resource-intensive processing necessary for its implementation. Historically, neural network software is executed in Central Processing Units (CPUs), which are general purpose devices. However, metrics such as inference speed, energy efficiency and circuit area are improved with the use of specialized hardware accelerators. In the context of edge computing, the processing on location of data gathered by Internet of Things (IoT) devices, there are significant restrictions as to those metrics. One of the devices frequently used for this purpose are Field-Programmable Gate Arrays (FPGAs), which offer integrated circuits whose functionality may be programmed for the synthesis of hardware specialized in specific algorithms, offering high performance and execution efficiency, as well as other benefits such as being able to be reconfigured after manufacturing, shorter design cycles, faster time-to-market and lower cost than the alternatives. One of the issues for the use of FPGAs is that its programming is difficult and requires specialist knowledge to fully make use of these devices characteristics, and not every team of artificial intelligence developers has such knowledge. Consequently, there is interest in systems that automatize the synthesis of neural network hardware accelerators in FPGAs, to bring the benefits of this technique to a wider audience. Another approach to solve the high restriction environment of edge computing is neural network quantization, which means reducing the precision of representation of parameters so they consume less memory, and operations using these numbers are faster. This work studies the state of the art of this manner of software, diagnosing existing gaps. The main objective of this research is the creation and validation of a proof of concept of automatic generation of neural network hardware accelerators that are using parameter quantization techniques to enable its synthesis on small FPGAs aimed towards edge computing. For the validation of this system, an accelerator was generated and its behavior was measured in metrics of latency, end result throughput, energy efficiency, circuit area and compared to the execution of the same neural network in a high-end personal computer CPU. The results indicate that the generated hardware accelerator is synthesizeable, significantly faster and consumes considerably less energy than the CPU implementation.