Automatic Hybrid-Precision Quantization for MIMO Detectors

In the design of wireless systems, quantization plays a critical role in hardware, which directly affects both area efficiency and energy efficiency. Being an enabling technique, the wide applications of multiple-input multiple-output (MIMO) heavily rely on efficient implementations balancing both performance and complexity. However, most of the existing detectors quantize all variables in a uniform way, resulting in high redundancy and low flexibility. Requiring both expertise and efforts, an in-depth tailored quantization usually asks for prohibitive costs and is not considered by conventional MIMO detectors. In this paper, a general framework named the automatic hybrid-precision quantization (AHPQ) is proposed with two parts: integral part quantization (IPQ) determined by probability density function (PDF), and fractional part quantization (FPQ) by deep reinforcement learning (DRL). Being automatic, AHPQ demonstrates high efficiency in figuring out good quantizations for a set of algorithmic parameters. For the approximate message passing (AMP) detector, AHPQ achieves up to 57.8% lower average bitwidth than the unified quantization (UQ) one with almost no performance sacrifice. The feasibility of AHPQ has been verified by implementation with 65 nm CMOS technology. Compared with its UQ counterpart, AHPQ exhibits $2.97\times$ higher throughput-to-area ratio (TAR) with 19.3% lower energy dissipation. Moreover, by node compression and strength reduction, the AHPQ-guided detector outperforms the state-of-the-art (SOA) in both throughput (17.92 Gb/s) and energy efficiency (7.93 pJ/b). The proposed AHPQ framework is also applicable for other digital signal processing (DSP) algorithms.