mlsanityData to 7 Oct 2026

CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs

arXiv 2201.01863 ↗First posted 5 Jan 2022cs.LG cs.AR cs.DCcited by 11

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Abstract (arXiv)

Need for the efficient processing of neural networks has given rise to the development of hardware accelerators. The increased adoption of specialized hardware has highlighted the need for more agile design flows for hardware-software co-design and domain-specific optimizations. In this paper, we present CFU Playground: a full-stack open-source framework that enables rapid and iterative design and evaluation of machine learning (ML) accelerators for embedded ML systems. Our tool provides a completely open-source end-to-end flow for hardware-software co-design on FPGAs and future systems research. This full-stack framework gives the users access to explore experimental and bespoke architectures that are customized and co-optimized for embedded ML. Our rapid, deploy-profile-optimization feedback loop lets ML hardware and software developers achieve significant returns out of a relatively small investment in customization. Using CFU Playground's design and evaluation loop, we show substantial speedups between 55× and 75×. The soft CPU coupled with the accelerator opens up a new, rich design space between the two components that we explore in an automated fashion using Vizier, an open-source black-box optimization service.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 14 of its 39 references that are in the map. First posted on arXiv 5 Jan 2022.

Builds on

14 of 39 references.

References · 14

  1. 2020TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systemscited by 161
  2. 2020The Hardware Lotterycited by 160
  3. 2016FINN: A Framework for Fast, Scalable Binarized Neural Network Inferencecited by 141
  4. 2021MLPerf Tiny Benchmarkcited by 118
  5. 2018CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUscited by 116
  6. 2020Benchmarking TinyML Systems: Challenges and Directioncited by 89
  7. 2021hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devicescited by 61
  8. 2019Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integrationcited by 59
  9. 2019FPGA-based Accelerators of Deep Learning Networks for Learning and Classification: A Reviewcited by 29
  10. 2018Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directionscited by 27
  11. 2020Agile SoC Development with Open ESPcited by 5
  12. 2020LiteX: an open-source SoC builder and library based on Migen Python DSLcited by 4
2 more
  1. 2020AI Tax: The Hidden Cost of AI Data Center Applicationscited by 3
  2. 2019Yosys+nextpnr: an Open Source Framework from Verilog to Bitstream for Commercial FPGAscited by 2

Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 4; 2024: 1; 2025: 3.

11 papers.

  1. 2023A Machine Learning-oriented Survey on Tiny Machine Learningcited by 26
  2. 2022Edge Impulse: An MLOps Platform for Tiny Machine Learningcited by 24
  3. 2023Chameleon: a Heterogeneous and Disaggregated Accelerator System for Retrieval-Augmented Language Modelscited by 23
  4. 2022Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robotscited by 17
  5. 2023Co-Design of Approximate Multilayer Perceptron for Ultra-Resource Constrained Printed Circuitscited by 11
  6. 2022Machine Learning Sensorscited by 5
  7. 2025Designing Object Detection Models for TinyML: Foundations, Comparative Analysis, Challenges, and Emerging Solutionscited by 1
  8. 2025RISC-V Based TinyML Accelerator for Depthwise Separable Convolutions in Edge AIcited by 1
  9. 2023AxOMaP: Designing FPGA-based Approximate Arithmetic Operators using Mathematical Programmingcited by 0
  10. 2024Accelerating TinyML Inference on Microcontrollers through Approximate Kernelscited by 0
  11. 2025Lifetime-Aware Design for Item-Level Intelligence at the Extreme Edgecited by 0