tinyML Asia 2023 – Georgios Flamis: How to enable seamless TinyML development and deployment
How to enable seamless TinyML development and deployment
Georgios FLAMIS, Sn. Staff System Design engineer , Renesas
The widespread adoption of Tiny Machine Learning (TinyML) in resource-constrained edge devices necessitates the development of a scalable MCU/MPU line-up and an efficient ecosystem and development tools tailored to address the unique challenges of deploying machine learning models in resource-limited environments. In this work, we present the scalable Renesas MCU/MPU lineup with a dedicated ecosystem and development tool framework designed to streamline the development, deployment, and management of TinyML applications.
The proposed product families with the ecosystem comprises a seamless integration of hardware, software, and cloud components, catering to the specific needs of TinyML. At the hardware level, we introduce a versatile set of devices to TinyML, allowing developers to adapt them to various edge devices, enabling efficient and low-power inference.
In tandem with the hardware, we present a comprehensive development tool framework explicitly tailored for TinyML development which ensure that the trained models are lightweight and suitable for deployment on edge devices with limited memory and processing capabilities. Herby targeting also the 16-bit devices from Renesas too.
To demonstrate the efficiency of our ecosystem and development tool framework, we present several real-world use cases, including VUI – voice command recognition on an Arm Cortex M23 device running at 48MHz, anomaly detection, and predictive maintenance on Arm Cortex M33 device, wild animal detection using the Dynamic Reconfigurable Processor – AI (DRP-AI), and person access system running just at 120MHz single core Arm Cortex M4 device from Renesas as edge devices. Benchmarking results showcase superior performance, with significant reductions in memory footprint, energy consumption, and inference latency compared to traditional TinyML implementations.
Furthermore, the extensibility and modularity of our application examples within our ecosystem allow easy integration with emerging TinyML algorithms in different multimodal applications and systems.
In conclusion, our device family together with our ecosystem and development tool framework provide a holistic solution for TinyML deployment, addressing the challenges of resource constraints while enabling efficient and intelligent applications on edge devices. This work contributes to advancing the field of TinyML, driving the proliferation of edge intelligence, and enabling novel and innovative use cases across various industries.
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