A RISC-V Domain-Specific Processor for Deep Learning-Based Channel Estimation

Channel estimation (CE) is a critical component in the massive multi-input multi-output (MIMO) communication systems. Compared with conventional CE algorithms, deep learning (DL)-based approach becomes a promising alternative, due to its capability of offering enhanced performance and robustness across diverse scenarios. However, efficient DL-based CE algorithms have two key properties that make them challenging for implementation in existing architectures at the edge side: the diversity of deep neural networks (DNNs) and CE strategies, and the involvements of multiple computation-intensive tasks that compass conventional signal processing, artificial intelligence (AI) inference, and online learning. To address these challenges, a domain-specific processor based on an extended RISC-V instruction set architecture (ISA) is proposed to perform these DL-based CE algorithms. First, a dedicated RISC-V ISA extension is developed to support all essential operations required by a DL-based CE algorithm, such as matrix inversion, in a flexible manner. Building on the customized ISA extension, a highly adaptable and scalable RISC-V processor is developed, featuring scalar and vector posit arithmetic units to alleviate high computational and memory demands of DNNs during both inference and training phase. Additionally, a coarse-grained matrix accelerator is integrated to expedite various matrix operations ensuring high throughput. In this way, both high flexibility and computational efficiency are achieved. Finally, our processor is implemented on a TSMC 28-nm technology. Implementation results show that the processor achieves a speedup of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.16\sim 6.80\times $ </tex-math></inline-formula> for all matrix operations compared with the state-of-the-art work. Moreover, the proposed processor provides an area efficiency improvement of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.61\times $ </tex-math></inline-formula> and an energy efficiency enhancement of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.6\sim 15.4\times $ </tex-math></inline-formula> compared to the open-source vector processor Ara. Notably, this work is the first RISC-V domain-specific processor tailored for diverse DL-based CE algorithms.