Dustin: A 16-Cores Parallel Ultra-Low-Power Cluster with 2b-to-32b Fully Flexible Bit-Precision and Vector Lockstep Execution Mode
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Abstract (arXiv)
Computationally intensive algorithms such as Deep Neural Networks (DNNs) are becoming killer applications for edge devices. Porting heavily data-parallel algorithms on resource-constrained and battery-powered devices poses several challenges related to memory footprint, computational throughput, and energy efficiency. Low-bitwidth and mixed-precision arithmetic have been proven to be valid strategies for tackling these problems. We present Dustin, a fully programmable compute cluster integrating 16 RISC-V cores capable of 2- to 32-bit arithmetic and all possible mixed-precision permutations. In addition to a conventional Multiple-Instruction Multiple-Data (MIMD) processing paradigm, Dustin introduces a Vector Lockstep Execution Mode (VLEM) to minimize power consumption in highly data-parallel kernels. In VLEM, a single leader core fetches instructions and broadcasts them to the 15 follower cores. Clock gating Instruction Fetch (IF) stages and private caches of the follower cores leads to 38% power reduction with minimal performance overhead (<3%). The cluster, implemented in 65 nm CMOS technology, achieves a peak performance of 58 GOPS and a peak efficiency of 1.15 TOPS/W.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jan 2022.
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Citing papers per year: 2023: 2; 2024: 0; 2025: 1.
3 papers.
- 2023A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platformscited by 21
- 2023A Precision-Scalable RISC-V DNN Processor with On-Device Learning Capability at the Extreme Edgecited by 10
- 2025Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applicationscited by 6
- 2025Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applications20 Aug 2025
- 2023A Precision-Scalable RISC-V DNN Processor with On-Device Learning Capability at the Extreme Edge15 Sep 2023
- 2023A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms27 Jun 2023