MCUFlowNet introduces a hardware‑aware neural architecture search framework, PACE‑NAS, that separates memory usage from latency for optical flow networks on microcontrollers. By fixing the backbone’s peak SRAM and searching for efficient operators and depths, the authors reduce peak SRAM usage by 18% and improve accuracy with lightweight context modules. The resulting MCUFlowNet models, particularly MCUFlowNet‑S, run 1.76× faster than a retrained EdgeFlowNet while achieving comparable or better performance in simulated drone flight tests, and the approach also benefits semantic segmentation tasks.
arXiv:2604. 15622v3 Announce Type: replace-cross Abstract: Always-on contextual AI runs language-aligned vision foundation models (VFMs) on edge devices, where the on-device model is the dominant continuous compute cost under strict latency and power limits.
ENAS is a hardware‑aware neural architecture search framework tailored for TinyML on microcontrollers. It uses a static feasibility check, a cell‑based search space with various block types and skip connections, and a three‑stage hybrid search strategy (random → top‑K → mutation) with cross‑run caching. The framework runs efficiently without GPUs, achieving significant search‑time speedups and competitive accuracy on Visual Wake Words and Melanoma Cancer benchmarks across a range of microcontrollers.
arXiv:2606. 15004v1 Announce Type: cross Abstract: Deploying neural networks on low-power microcontrollers (MCUs) requires selecting model architectures under tight memory, latency, and energy constraints.
Vision-Language-Action (VLA) models generalize across tasks, but their autoregressive decoders need sequential forward passes in proportion to the action horizon, each bounded below on embedded aerial...
arXiv:2610.34717v1 Announce Type: cross Abstract: Vision--language--action (VLA) models increasingly run on edge devices aboard robots, where inference latency constrains closed-loop robot control. H...
arXiv:2607. 01984v1 Announce Type: cross Abstract: Newer lightweight convolutional neural networks are often presented as improving predictive performance and deployment efficiency, but such claims require controlled evaluation.
arXiv:2610.33338v1 Announce Type: cross Abstract: High-performance optical flow networks are difficult to deploy on resource-constrained edge accelerators because their iterative refinement warps fea...
arXiv:2603. 15106v2 Announce Type: replace Abstract: Enabling efficient deep neural network (DNN) inference on edge devices with different hardware constraints is a challenging task that typically requires DNN architectures to be specialized for each device separately.
The paper presents a novel multi‑exit computational scheme for TinyML on an ultra‑low‑power GAP9 SoC, adding confidence‑based gating points to a MobileNetV2 CNN for ImageNet‑100. By allowing inference to stop early, the approach cuts average MAC operations by 41 % (from 313 MMAC to 185 MMAC), reduces inference time by 29 % (49 ms to 35 ms), and saves 24 % in energy (2.1 mJ to 1.6 mJ per frame) with only a ~1 % drop in accuracy. Compared to a state‑of‑the‑art adaptive CNN on the same hardware, the method more than doubles computational efficiency, raising MAC/cycle from 8.1 to 17.2.
FORGE is a forward‑only test‑time adaptation technique designed for integer‑only vision models running on microcontrollers. It restores batch‑normalization statistics after BN folding by re‑normalizing each convolution’s per‑channel output using only forward‑pass estimates, enabling adaptation on deployed, folded integer models. The method achieves accuracy gains comparable to gradient‑based TENT, requires adapting only a few layers, works with single‑sample streaming, and has been validated on an ESP32‑S3 with minimal energy and latency overhead.
arXiv:2608. 08961v1 Announce Type: new Abstract: AI training's rising resource intensity is straining electricity supplies and carbon budgets, motivating systematic study of memory-efficient training on constrained hardware.
arXiv:2603. 26551v2 Announce Type: replace-cross Abstract: Vision backbone networks play a central role in modern computer vision.