Algorithm-Architecture Co-Design for Domain-Specific Accelerators in Communication and Artificial Intelligence
dc.contributor.author | Tao, Yaoyu | |
dc.date.accessioned | 2022-05-25T15:21:21Z | |
dc.date.available | 2022-05-25T15:21:21Z | |
dc.date.issued | 2022 | |
dc.date.submitted | 2022 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/172593 | |
dc.description.abstract | 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. | |
dc.language.iso | en_US | |
dc.subject | Algorithm-architecture co-design, channel coding, machine learning, polar decoder, LDPC decoder, nonbinary LDPC decoder, post-processing | |
dc.subject | successive-cancellation, differentiable neural computer, neural ordinary differential equation, DNC-aided flip polar decoding | |
dc.title | Algorithm-Architecture Co-Design for Domain-Specific Accelerators in Communication and Artificial Intelligence | |
dc.type | Thesis | |
dc.description.thesisdegreename | PhD | en_US |
dc.description.thesisdegreediscipline | Electrical and Computer Engineering | |
dc.description.thesisdegreegrantor | University of Michigan, Horace H. Rackham School of Graduate Studies | |
dc.contributor.committeemember | Zhang, Zhengya | |
dc.contributor.committeemember | Mahlke, Scott | |
dc.contributor.committeemember | Flynn, Michael | |
dc.contributor.committeemember | Kim, Hun Seok | |
dc.subject.hlbsecondlevel | Electrical Engineering | |
dc.subject.hlbtoplevel | Engineering | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/172593/1/taoyaoyu_1.pdf | |
dc.identifier.doi | https://dx.doi.org/10.7302/4622 | |
dc.identifier.orcid | 0000-0001-7500-5250 | |
dc.identifier.name-orcid | Tao, Yaoyu; 0000-0001-7500-5250 | en_US |
dc.working.doi | 10.7302/4622 | en |
dc.owningcollname | Dissertations and Theses (Ph.D. and Master's) |
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