Alessandro Capotondi*, Manuele Rusci**, Marco Fariselli**, Luca Benini**

*: Università di Modena e Reggio-Emilia, **: Università di Bologna

TinyML; Edge Computing; ARM Cortex-M; STM32H7; STM32F7; STM32L4; STM32F4

Running inference tasks at the edge of the sensing infrastructure minimizes the user's network bandwidth and improves the response time. When envisioning battery-powered IoT devices with smart capabilities, the limited energy budget puts constraints on the design of computing platforms for edge computing. Microcontroller Units (MCUs), which dominate the low-power spectrum of the computing platforms, typically feature limited memory resources (up to few MB of FLASH and below 1 MB of on-chip RAM) and lack floating-point hardware support. Hence, the deployment of high computational and memory requirements of deep learning workloads on edge devices results exceptionally challenging.

Low-precision integer arithmetic is a necessary ingredient for enabling Deep Learning inference on tiny and resource-constrained IoT edge devices. The approach described by Rusci et al. leveraged heterogeneous mixed-precision to deploy deep inference networks on tiny MCUs. The proposed technique aims at cutting the number of bits of individual weight or activation tensors below 8 bits up to fit the memory constraints and, at the same time, paying a limited accuracy drop if compared to the full-precision network. However, despite the numerous works addressing quantization on the server-side, no solution is provided for the deployment phase, especially if considering the mixed-precision sub-byte scenario. To tackle this problem, we present CMix-NN, an open-source mixed-precision library for quantized neural networks deployment on microcontroller targets. Differently, from state-of-the-art deployment solutions available for MCUs, our library supports convolutional kernels with any bit precision* in the set of 8, 4 and 2 bits, for any of the convolution operands.

Please, cite the following papers if you are going to use this library.

• CMix-NN Accepted ISCAS20

• Mixed-Precision Training Methodology Rusci, Manuele, Alessandro Capotondi, and Luca Benini. "Memory-driven mixed low precision quantization for enabling deep network inference on microcontrollers." arXiv preprint arXiv:1905.13082 (2019).

  @misc{rusci2019memorydriven,
      title={Memory-Driven Mixed Low Precision Quantization For Enabling Deep Network Inference On Microcontrollers},
      author={Manuele Rusci and Alessandro Capotondi and Luca Benini},
      year={2019},
      eprint={1905.13082},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
  }
  

The CMix-NN is a C inference library for ARM Cortex-M MCU:

• Include: contains the public header file of CMix-NN
• Source: contains the implementation of convolutional kernels supported by CMix-NN
• scripts/codegen: contains the code genetator for the CMix-NN sources.

• Add MobilenetV1 example (coming soon)
• Add dense functions (coming soon)
• Add the scripts to formart the parameters
• Add pooling functions