Non-Linear CNN-Based Read Channel for Hard Disk Drive With 30% Error Rate Reduction and Sequential 200-Mbits/s Throughput in 28-nm CMOS

Abstract

In this work, we present the world first ASIC implementation of a machine learning (ML)-based data detection channel for hard disk drives (HDDs) using a customized convolutional neural network (CNN). The chip demonstrates a 30.3% error rate reduction over the state-of-the-art HDD detection channel with two-dimensional magnetic recording (TDMR) setting. The work incorporates a full-scale co-optimization flow between the ML algorithms and the customized hardware design for achieving: 1) superior detection accuracy; 2) high-throughput sequential detections; and 3) improved power efficiency at the same time. This work also demonstrates for the first time the non-linear detection capability being embedded into the HDD reading channels. The key features of our chip include: 1) a fully unrolled CNN with dedicated silicon and hardware implementation for each convolutional layer to produce fast sequential time-series data detection at 200-Mbits/s; 2) in total six depthwise-separable convolutional layers implemented with two types of systolic arrays and 100% PE utilization for continuous data flow and high pipelining; 3) integer-only convolutions with reduced-precision weights (8 bit) and internal data (6 bit) for detection at an improved detection power efficiency at 0.86 nJ/bit and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim $ </tex-math></inline-formula> 4TOPS/W; and 4) customized quantized ReLU (QReLU) units designed to maintain low-precision feature maps and recovery accuracy loss from the model quantization.