ADNA: Automating Application-Specific Integrated Circuit Development of Neural Network Accelerators

Recently, multiple new technologies have emerged for automating the development of neural network (NN) accelerators for both field-programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs). This paper explores methodologies for translating NN algorithms into chip layouts, with a focus on end-to-end automation, cost-effectiveness, and open-source software. We present a robust framework for developing NN-to-silicon solutions and demonstrate a seamless plug-and-play automation flow using TensorFlow, Vivado HLS, HLS4ML, and Openlane2. SkyWater Technologies’ 130 nm PDK (Sky130) is employed to successfully generate layouts for two small NN examples under 1000 parameters, incorporating dense, activation, and 2D convolution layers. The results affirm that current open-source tools effectively automate low-complexity neural network architectures and deliver faster performance through FPGA structures. However, this improved performance comes at the cost of increased die area compared to bare-metal designs. While this showcases significant progress in accessible NN automation, achieving manufacturing-ready layouts for more complex NN architectures remains a challenge due to current tool limitations and heightened computational demands, which points to exciting opportunities for future advancements.