msf-CNN: Patch-based Multi-Stage Fusion with Convolutional Neural Networks for TinyML

AI spans from large language models to tiny models running on microcontrollers (MCUs). Extremely memory-efficient model architectures are decisive to fit within an MCU's tiny memory budget e.g., 128kB of RAM. However, inference latency must remain small to fit real-time constraints. An approach to tackle this is patch-based fusion, which aims to optimize data flows across neural network layers. In this paper, we introduce msf-CNN, a novel technique that efficiently finds optimal fusion settings for convolutional neural networks (CNNs) by walking through the fusion solution space represented as a directed acyclic graph. Compared to previous work on CNN fusion for MCUs, msf-CNN identifies a wider set of solutions. We published an implementation of msf-CNN running on various microcontrollers (ARM Cortex-M, RISC-V, ESP32). We show that msf-CNN can achieve inference using 50% less RAM compared to the prior art (MCUNetV2 and StreamNet). We thus demonstrate how msf-CNN offers additional flexibility for system designers.

TinyChirp: Bird Song Recognition Using TinyML Models on Low-power Wireless Acoustic Sensors

Monitoring biodiversity at scale is challenging. Detecting and identifying species in fine grained taxonomies requires highly accurate machine learning (ML) methods. Training such models requires large high quality data sets. And deploying these models to low power devices requires novel compression techniques and model architectures. While species classification methods have profited from novel data sets and advances in ML methods, in particular neural networks, deploying these state of the art models to low power devices remains difficult. Here we present a comprehensive empirical comparison of various tinyML neural network architectures and compression techniques for species classification. We focus on the example of bird song detection, more concretely a data set curated for studying the corn bunting bird species. The data set is released along with all code and experiments of this study. In our experiments we compare predictive performance, memory and time complexity of classical spectrogram based methods and recent approaches operating on raw audio signal. Our results indicate that individual bird species can be robustly detected with relatively simple architectures that can be readily deployed to low power devices.

On-Device Evaluation Toolkit for Machine Learning on Heterogeneous Low-Power System-on-Chip

Network delays, throughput bottlenecks and privacy issues push Artificial Intelligence of Things (AIoT) designers towards evaluating the feasibility of moving model training and execution (inference) as near as possible to the terminals. Meanwhile, results from the TinyML community demonstrate that, in some cases, it is possible to execute model inference directly on the terminals themselves, even if these are small microcontroller-based devices. However, to date, researchers and practitioners in the domain lack convenient all-in-one toolkits to help them evaluate the feasibility of moving execution of arbitrary models to arbitrary low-power IoT hardware. To this effect, we present in this paper U-TOE, a universal toolkit we designed to facilitate the task of AIoT designers and researchers, by combining functionalities from a low-power embedded OS, a generic model transpiler and compiler, an integrated performance measurement module, and an open-access remote IoT testbed. We provide an open source implementation of U-TOE and we demonstrate its use to experimentally evaluate the performance of a wide variety of models, on a wide variety of low-power boards, based on popular microcontroller architectures (ARM Cortex-M and RISC-V). U-TOE thus allows easily reproducible and customisable comparative evaluation experiments in this domain, on a wide variety of IoT hardware all-at-once. The availability of a toolkit such as U-TOE is desirable to accelerate the field of AIoT, towards fully exploiting the potential of edge computing.