Embedded Arena: Iterative Optimization via Hardware Feedback

• URL: http://arxiv.org/abs/2606.16190v1
• Date: Mon, 15 Jun 2026 04:04:23 GMT
• Title: Embedded Arena: Iterative Optimization via Hardware Feedback
• Abstract summary: Agentic co-optimization achieves 250x compression for vision models with 3.3% accuracy loss and 400x for audio with 6% Feature Error Rate loss.<n>We demonstrate practical impact in two real-world systems: an elk-detection camera trap (96.7% accuracy) and a phonetic-transcription wearable (8.44% FER)
• Score: 44.54626665766497
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Embedded devices from wildlife monitoring stations to clinical wearables require local AI inference due to latency, communication, or privacy constraints. Optimizing models for heterogeneous microcontrollers (MCUs) requires simultaneously satisfying hard physical constraints on memory, power, and temperature while preserving accuracy, a multidimensional optimization that is today performed manually by experts. We ask whether an LLM agent can autonomously navigate this complex, multi-turn pipeline guided by real hardware feedback, and introduce a hardware-in-the-loop agent arena in which the agent iteratively refines both model and firmware -- compiling, flashing, and measuring on real hardware -- to enable closed-loop optimization. Frontier models, including Claude Opus 4.7 and Gemini 3.1 Pro, fail entirely without hardware feedback (0% deployment success), whereas our hardware-in-the-loop formulation achieves the first successful deployment within three iterations and can surpass human expert results within seven. This agentic co-optimization achieves 250x compression for vision models with <3.3% accuracy loss and 400x for audio with <6% Feature Error Rate loss, enabling battery-free operation on a commercial MCU via solar harvesting. We demonstrate practical impact in two real-world systems: an elk-detection camera trap (96.7% accuracy) and a phonetic-transcription wearable (8.44% FER) for child development research.

Related papers

• Modality-Decoupled Federated Learning for Privacy-Preserving Embodied Intelligence in 6G [90.42669277637026]
  Sixth-generation (6G) wireless networks are expected to provide a key infrastructure for large-scale embodied intelligence.<n>This article proposes FedMVLA, a modality-decoupled FL framework for privacy-preserving embodied intelligence in 6G networks.
  arXiv  Detail & Related papers  (2026-09-09T01:36:44Z)
• DREAM Technical Report [53.37525197047151]
  We present DREAM, an autonomous optimization control architecture that adds a perception-aware, orchestrable, and auditable policy layer atop existing pipelines without replacing them.<n> DREAM has two core components. First, a three-tier Intent Engine fuses on-device signals into structured L0/L1/L2 intent representations; its edge-cloud trigger chain reduces reporting volume to approximately 8.7%.<n>Second, a Meta Engine uses a MetaModel for layered M1-to-M2-to-M3 reasoning: intent summarization, strategy planning informed by Strategy Memory, and parameter translation. It dispatches the resulting parameters through a unified outlet with
  arXiv  Detail & Related papers  (2026-08-10T10:35:32Z)
• C2E: Boosting Ego-Only 3D Object Detection via Multi-Teacher Contrastive Knowledge Distillation [30.753798055264184]
  We develop a novel C2E (Co-Perception to Eo-Perception) paradigm through the Multi-to-Single (M2S) agent contrastive knowledge distillation framework.<n>Our M2S framework can deliver up to a 8.64% improvement in 3D mAP performance without introducing any communication costs.
  arXiv  Detail & Related papers  (2026-07-02T07:48:22Z)
• OpenGlass: Ultra-Low-Power On-Device AI Eyewear with Event-based Vision [8.93031966791759]
  This work introduces an open-source smart glasses platform for rapid prototyping of novel sensors and algorithms.<n>Its modular design uses a flexible FPC interposer to support both event-based and frame-based cameras without full PCB.<n>The prototype achieves up to 11.5 hours of continuous on-device ML from a 200 mAh battery.
  arXiv  Detail & Related papers  (2026-06-05T16:27:02Z)
• Deep Learning-Based Anomaly Detection in Spacecraft Telemetry on Edge Devices [0.038233569758620044]
  This paper investigates three approaches for spacecraft telemetry anomaly detection -- forecasting & threshold, direct classification, and image classification.<n>We optimize them for edge deployment using multi-objective neural architecture optimization on the European Space Agency Anomaly dataset.<n>Our optimized models require just 0.36-6.25% of CubeSat RAM, making on-board anomaly detection practical even on highly constrained hardware.
  arXiv  Detail & Related papers  (2026-03-31T07:45:40Z)
• Lightweight Transformer Architectures for Edge Devices in Real-Time Applications [0.0]
  This survey examines lightweight transformer architectures specifically designed for edge deployment.<n>We systematically review prominent lightweight variants including MobileBERT, TinyBERT, DistilBERT, EfficientFormer, EdgeFormer, and MobileViT.<n> Experimental results demonstrate that modern lightweight transformers can achieve 75-96% of full-model accuracy while reducing model size by 4-10x and inference latency by 3-9x.
  arXiv  Detail & Related papers  (2026-01-05T01:04:25Z)
• SD3.5-Flash: Distribution-Guided Distillation of Generative Flows [87.45964232927945]
  We present SD3.5-Flash, an efficient few-step distillation framework that brings high-quality image generation to consumer devices.<n>We introduce two key innovations: "timestep sharing" to reduce gradient noise and "split-timestep fine-tuning" to improve prompt alignment.<n>This democratizes access across the full spectrum of devices, from mobile phones to desktop computers.
  arXiv  Detail & Related papers  (2025-09-25T16:07:38Z)
• EfficientLLM: Efficiency in Large Language Models [64.3537131208038]
  Large Language Models (LLMs) have driven significant progress, yet their growing counts and context windows incur prohibitive compute, energy, and monetary costs.<n>We introduce EfficientLLM, a novel benchmark and the first comprehensive empirical study evaluating efficiency techniques for LLMs at scale.
  arXiv  Detail & Related papers  (2025-05-20T02:27:08Z)
• Hybrid Knowledge Transfer through Attention and Logit Distillation for On-Device Vision Systems in Agricultural IoT [0.0]
  This work advances real-time, energy-efficient crop monitoring in precision agriculture.<n>It demonstrates how we can attain ViT-level diagnostic precision on edge devices.
  arXiv  Detail & Related papers  (2025-04-21T06:56:41Z)
• ALOcc: Adaptive Lifting-Based 3D Semantic Occupancy and Cost Volume-Based Flow Predictions [91.55655961014027]
  3D semantic occupancy and flow prediction are fundamental to understanding scene scene.<n>This paper proposes a vision-based framework with three targeted improvements.<n>Our purely convolutional architecture establishes new SOTA performance on multiple benchmarks for both semantic occupancy and joint semantic-flow prediction.
  arXiv  Detail & Related papers  (2024-11-12T11:32:56Z)
• Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices [90.30316433184414]
  We propose a data-model-hardware tri-design framework for high- throughput, low-cost, and high-accuracy MOT on HD video stream. Compared to the state-of-the-art MOT baseline, our tri-design approach can achieve 12.5x latency reduction, 20.9x effective frame rate improvement, 5.83x lower power, and 9.78x better energy efficiency, without much accuracy drop.
  arXiv  Detail & Related papers  (2022-10-16T16:21:40Z)
• SensiX: A Platform for Collaborative Machine Learning on the Edge [69.1412199244903]
  We present SensiX, a personal edge platform that stays between sensor data and sensing models. We demonstrate its efficacy in developing motion and audio-based multi-device sensing systems. Our evaluation shows that SensiX offers a 7-13% increase in overall accuracy and up to 30% increase across different environment dynamics at the expense of 3mW power overhead.
  arXiv  Detail & Related papers  (2020-12-04T23:06:56Z)

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.