Cheshire: A Lightweight, Linux-Capable RISC-V Host Platform for Domain-Specific Accelerator Plug-In
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Abstract (arXiv)
Power and cost constraints in the internet-of-things (IoT) extreme-edge and TinyML domains, coupled with increasing performance requirements, motivate a trend toward heterogeneous architectures. These designs use energy-efficient application-class host processors to coordinate compute-specialized multicore accelerators, amortizing the architectural costs of operating system support and external communication. This brief presents Cheshire, a lightweight and modular 64-bit Linux-capable host platform designed for the seamless plug-in of domain-specific accelerators. It features a unique low-pin-count DRAM interface, a last-level cache configurable as scratchpad memory, and a DMA engine enabling efficient data movement to or from accelerators or DRAM. It also provides numerous optional IO peripherals including UART, SPI, I2C, VGA, and GPIOs. Cheshire's synthesizable RTL description, comprising all of its peripherals and its fully digital DRAM interface, is available free and open-source. We implemented and fabricated Cheshire as a silicon demonstrator called Neo in TSMC's 65nm CMOS technology. At 1.2 V, Neo achieves clock frequencies of up to 325 MHz while not exceeding 300 mW in total power on data-intensive computational workloads. Its RPC DRAM interface consumes only 250 pJ/B and incurs only 3.5 kGE in area for its PHY while attaining a peak transfer rate of 750 MB/s at 200 MHz.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 May 2023.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 2; 2026: 2.
7 papers.
- 2024Flex-PE: Flexible and SIMD Multi-Precision Processing Element for AI Workloadscited by 15
- 2023Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Reviewcited by 9
- 2025XR-NPE: High-Throughput Mixed-precision SIMD Neural Processing Engine for Extended Reality Perception Workloadscited by 3
- 2024A Heterogeneous RISC-V based SoC for Secure Nano-UAV Navigationcited by 2
- 2026SPADE: A SIMD Posit-enabled compute engine for Accelerating DNN Efficiencycited by 1
- 2025CORDIC Is All You Needcited by 0
- 2026MATCHA: Efficient Deployment of Deep Neural Networks on Multi-Accelerator Heterogeneous Edge SoCscited by 0
- 2026MATCHA: Efficient Deployment of Deep Neural Networks on Multi-Accelerator Heterogeneous Edge SoCs10 Apr 2026
- 2026SPADE: A SIMD Posit-enabled compute engine for Accelerating DNN Efficiency24 Jan 2026
- 2025XR-NPE: High-Throughput Mixed-precision SIMD Neural Processing Engine for Extended Reality Perception Workloads18 Aug 2025
- 2025CORDIC Is All You Need4 Mar 2025
- 2024Flex-PE: Flexible and SIMD Multi-Precision Processing Element for AI Workloads16 Dec 2024
- 2024A Heterogeneous RISC-V based SoC for Secure Nano-UAV Navigation7 Jan 2024
- 2023Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review20 Nov 2023