Algorithm-Architecture Co-Design for Domain-Specific Accelerators in Communication and Artificial Intelligence
Yaoyu Tao · Deep Blue (University of Michigan) · 2022
The past decade has witnessed an explosive growth of data and the needs for high-speed data communications and processing. The needs continue to drive the development of new hardware for transmitting more data reliably and processing more data to obtain a higher level of intelligence. This thesis work explores algorithm-architecture co-design approach to derive efficient solutions for domain-specific communication and machine learning accelerators. It focuses on advanced and most compute-intensive accelerator designs for: 1) channel coding for data transmission, including polar codes and low-density parity-check (LDPC) codes, and 2) neural networks for machine learning, including differentiable neural computer (DNC) and neural ordinary differential equations (NODE). It also covers an interdisciplinary area of AI-aided communication, exploring DNC-aided flip decoding for polar codes. This work introduces a split-tree successive-cancellation list (SCL) decoder that works by dividing a polar code's decoding tree to sub-trees following a split-tree algorithm. Through algorithm-architecture co-optimizations, a 0.64mm2 40nm test chip implements a split-4, list-2, 8-frame-interleaved decoder that supports configurable code lengths up to 1024 bit and variable code rates. This work advances LDPC codes in two aspects: 1) we take the inspiration from simulated annealing to generalize the post-processor design using three methods: quenching, extended heating, and focused heating, each of which targets a different decoding error structure. The resulting post-processor is demonstrated to lower the error rate by two orders of magnitudes. 2) We also present a fully parallel decoder for a (160, 80) regular-(2, 4) NB-LDPC code over Galois field GF(64) in 65nm CMOS. The decoder employs fine-grained dynamic clock gating and decoder early termination to achieve a throughput of 1.22Gb/s and an energy efficiency of 3.03nJ/b. This work contributes to the hardware acceleration of DNC. We present HiMA, a tiled, history-based memory access engine with distributed memories in tiles. HiMA incorporates a traffic-aware multi-mode network-on-chip (NoC), an optimal submatrix-based memory partition and a two-stage usage sort method leveraging distributed tiles. We create a distributed DNC (DNC-D) to allow almost all memory operations to be applied to local memories. In a 40nm design, HiMA running DNC and DNC-D demonstrates 39.1x higher speed, 164.3x better area efficiency, and 61.2x better power efficiency over the state-of-the-art accelerators. This work contributes to the hardware acceleration of NODE for improved modeling capability of continuous-time events. We carry out algorithm-architecture co-design: 1) we propose adaptive neural activation sparsity for up to 80% complexity reduction while maintaining excellent training accuracy. 2) we develop a multi-mode PE design for NODE compute kernels with configurable interconnects between PEs to handle a variety of numerical ODE solvers. The hardware efficiency is further enhanced by exploring hardware reuse and hierarchical memory. Lastly, this work investigates an interdisciplinary area of AI-aided communication, applying DNC for flip decoding of polar codes. We develop new state and action encoding with a two-phase decoding flow. Simulation results show that proposed DNC-aided SCL-Flip (DNC-SCLF) decoding demonstrates up to 0.34dB coding gain improvement or 54.2% reduction in average number of decoding attempts compared to prior works. The five pieces of work presented in this thesis tackle hardware acceleration challenges in domain-specific computing using algorithm-architecture co-design techniques. The results of this work contribute to the developments of hardware accelerators in channel coding and deep learning, enabling next-generation communication and artificial intelligence from theory to efficient hardware.