TinyML Platforms Benchmarking

• URL: http://arxiv.org/abs/2112.01319v1
• Date: Tue, 30 Nov 2021 15:26:26 GMT
• Title: TinyML Platforms Benchmarking
• Authors: Anas Osman, Usman Abid, Luca Gemma, Matteo Perotto, and Davide Brunelli
• Abstract summary: Recent advances in ultra-low power embedded devices for machine learning (ML) have permitted a new class of products. TinyML provides a unique solution by aggregating and analyzing data at the edge on low-power embedded devices. Many TinyML frameworks have been developed for different platforms to facilitate the deployment of ML models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in state-of-the-art ultra-low power embedded devices for machine learning (ML) have permitted a new class of products whose key features enable ML capabilities on microcontrollers with less than 1 mW power consumption (TinyML). TinyML provides a unique solution by aggregating and analyzing data at the edge on low-power embedded devices. However, we have only recently been able to run ML on microcontrollers, and the field is still in its infancy, which means that hardware, software, and research are changing extremely rapidly. Consequently, many TinyML frameworks have been developed for different platforms to facilitate the deployment of ML models and standardize the process. Therefore, in this paper, we focus on bench-marking two popular frameworks: Tensorflow Lite Micro (TFLM) on the Arduino Nano BLE and CUBE AI on the STM32-NucleoF401RE to provide a standardized framework selection criterion for specific applications.

Related papers

• DeeR-VLA: Dynamic Inference of Multimodal Large Language Models for Efficient Robot Execution [114.61347672265076]
  Development of MLLMs for real-world robots is challenging due to the typically limited computation and memory capacities available on robotic platforms. We propose a Dynamic Early-Exit Framework for Robotic Vision-Language-Action Model (DeeR) that automatically adjusts the size of the activated MLLM. DeeR demonstrates significant reductions in computational costs of LLM by 5.2-6.5x and GPU memory of LLM by 2-6x without compromising performance.
  arXiv  Detail & Related papers  (2024-11-04T18:26:08Z)
• MiniCPM-V: A GPT-4V Level MLLM on Your Phone [83.10007643273521]
  MiniCPM-V is a series of efficient MLLMs deployable on end-side devices. By integrating the latest MLLM techniques in architecture, pretraining and alignment, MiniCPM-V 2.5 has several notable features. MiniCPM-V can be viewed as a representative example of a promising trend.
  arXiv  Detail & Related papers  (2024-08-03T15:02:21Z)
• MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases [81.70591346986582]
  We introduce MobileAIBench, a benchmarking framework for evaluating Large Language Models (LLMs) and Large Multimodal Models (LMMs) on mobile devices. MobileAIBench assesses models across different sizes, quantization levels, and tasks, measuring latency and resource consumption on real devices.
  arXiv  Detail & Related papers  (2024-06-12T22:58:12Z)
• LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit [55.73370804397226]
  Quantization, a key compression technique, can effectively mitigate these demands by compressing and accelerating large language models. We present LLMC, a plug-and-play compression toolkit, to fairly and systematically explore the impact of quantization. Powered by this versatile toolkit, our benchmark covers three key aspects: calibration data, algorithms (three strategies), and data formats.
  arXiv  Detail & Related papers  (2024-05-09T11:49:05Z)
• LM-Polygraph: Uncertainty Estimation for Language Models [71.21409522341482]
  Uncertainty estimation (UE) methods are one path to safer, more responsible, and more effective use of large language models (LLMs) We introduce LM-Polygraph, a framework with implementations of a battery of state-of-the-art UE methods for LLMs in text generation tasks, with unified program interfaces in Python. It introduces an extendable benchmark for consistent evaluation of UE techniques by researchers, and a demo web application that enriches the standard chat dialog with confidence scores.
  arXiv  Detail & Related papers  (2023-11-13T15:08:59Z)
• MLonMCU: TinyML Benchmarking with Fast Retargeting [1.4319942396517]
  It is non-trivial to choose the optimal combination of frameworks and targets for a given application. A tool called MLonMCU is proposed in this paper and demonstrated by benchmarking the state-of-the-art TinyML frameworks TFLite for Microcontrollers and TVM effortlessly.
  arXiv  Detail & Related papers  (2023-06-15T08:44:35Z)
• TinyReptile: TinyML with Federated Meta-Learning [9.618821589196624]
  We propose TinyReptile, a simple but efficient algorithm inspired by meta-learning and online learning. We demonstrate TinyReptile on Raspberry Pi 4 and Cortex-M4 MCU with only 256-KB RAM.
  arXiv  Detail & Related papers  (2023-04-11T13:11:10Z)
• TinyML: Tools, Applications, Challenges, and Future Research Directions [2.9398911304923456]
  TinyML is an embedded machine learning technique that enables ML applications on cheap, resource- and power-constrained devices. This article reviews various avenues available for TinyML implementation.
  arXiv  Detail & Related papers  (2023-03-23T15:29:48Z)
• A review of TinyML [0.0]
  The TinyML concept for embedded machine learning attempts to push such diversity from usual high-end approaches to low-end applications. TinyML is a rapidly expanding interdisciplinary topic at the convergence of machine learning, software, and hardware. This paper explores how TinyML can benefit a few specific industrial fields, its obstacles, and its future scope.
  arXiv  Detail & Related papers  (2022-11-05T06:02:08Z)
• How to Manage Tiny Machine Learning at Scale: An Industrial Perspective [5.384059021764428]
  Tiny machine learning (TinyML) has gained widespread popularity where machine learning (ML) is democratized on ubiquitous microcontrollers. TinyML models have been developed with different structures and are often distributed without a clear understanding of their working principles. We propose a framework using Semantic Web technologies to enable the joint management of TinyML models and IoT devices at scale.
  arXiv  Detail & Related papers  (2022-02-18T10:36:11Z)
• A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays [66.62377866022221]
  Latent Replay-based Continual Learning (CL) techniques enable online, serverless adaptation in principle. We introduce a HW/SW platform for end-to-end CL based on a 10-core FP32-enabled parallel ultra-low-power processor. Our results show that by combining these techniques, continual learning can be achieved in practice using less than 64MB of memory.
  arXiv  Detail & Related papers  (2021-10-20T11:01:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.