Real-Time Performance Benchmarking of TinyML Models in Embedded Systems (PICO: Performance of Inference, CPU, and Operations)

• URL: http://arxiv.org/abs/2509.04721v1
• Date: Fri, 05 Sep 2025 00:30:39 GMT
• Title: Real-Time Performance Benchmarking of TinyML Models in Embedded Systems (PICO: Performance of Inference, CPU, and Operations)
• Authors: Abhishek Dey, Saurabh Srivastava, Gaurav Singh, Robert G. Pettit,
• Abstract summary: PICO-TINYML-BENCHMARK is a framework for benchmarking the real-time performance of TinyML models on resource-constrained embedded systems.<n>We benchmark three representative TinyML models on two widely adopted platforms, BeagleBone AI64 and Raspberry Pi 4.<n>Results reveal critical trade-offs: the BeagleBone AI64 demonstrates consistent inference latency for AI-specific tasks, while the Raspberry Pi 4 excels in resource efficiency and cost-effectiveness.
• Score: 5.637804042390397
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents PICO-TINYML-BENCHMARK, a modular and platform-agnostic framework for benchmarking the real-time performance of TinyML models on resource-constrained embedded systems. Evaluating key metrics such as inference latency, CPU utilization, memory efficiency, and prediction stability, the framework provides insights into computational trade-offs and platform-specific optimizations. We benchmark three representative TinyML models -- Gesture Classification, Keyword Spotting, and MobileNet V2 -- on two widely adopted platforms, BeagleBone AI64 and Raspberry Pi 4, using real-world datasets. Results reveal critical trade-offs: the BeagleBone AI64 demonstrates consistent inference latency for AI-specific tasks, while the Raspberry Pi 4 excels in resource efficiency and cost-effectiveness. These findings offer actionable guidance for optimizing TinyML deployments, bridging the gap between theoretical advancements and practical applications in embedded systems.

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