arXiv Computer Vision
TinyFlow: Breaking the Warping Bottleneck for Edge Optical Flow
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arXiv Computer Vision
1d ago
MCUFlowNet: Hardware-Aware Neural Architecture Search for Optical Flow on Microcontrollers
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 Machine Learning
Jul 1
FlexViT: A Flexible FPGA-based Accelerator for Edge Vision Transformers
arXiv:2606. 31938v1 Announce Type: cross Abstract: Deploying Vision Transformer (ViT) models on edge platforms remains challenging due to their high computational demands and the architectural heterogeneity of modern hybrid ViT models, which incorporate both fully connected and convolutional layers.
arXiv Machine Learning
Oct 16
ShiftQAT: Quantization-Aware Training for the Numerical Contract of a Fixed-Function Edge CNN Accelerator
ShiftQAT is a quantization‑aware training workflow designed for the MAX7800x family of fixed‑function CNN accelerators. It enforces the accelerator’s numerical contract—power‑of‑two, per‑tensor symmetric scales, 8‑bit activations and biases, integer‑only arithmetic, and a single statically configured shift per layer—by training weights and biases on a joint power‑of‑two grid and resolving dependencies so that activation scales propagate statically. Compared to other methods, ShiftQAT uniquely satisfies all contract constraints while matching the accuracy of less constrained QAT approaches, achieving ImageNet performance within 2.9 percentage points of FP32 at W4A8 and enabling fast, energy‑efficient inference on MAX78002 silicon.
Hugging Face Trending Papers
Sep 10
FreeFlow: A Bias-free Hierarchical Transformer for Optical Flow Estimation
FreeFlow is a hierarchical transformer for optical flow estimation that eliminates traditional flow-specific inductive biases such as correlation volumes and feature warping. It relies on a single feed-forward encoder–decoder architecture that incorporates window attention, shifted-window attention, and reduced-resolution global attention. This design allows the model to scale with capacity while achieving state‑of‑the‑art accuracy on Sintel, KITTI‑2015, and Spring benchmarks, all while remaining memory efficient at 1080p inference.
arXiv Machine Learning
Sep 24
RAMP: Robust Adaptive Mixed-Precision Quantization for Edge CPU Vision Models
The paper introduces RAMP, a method for robust adaptive mixed‑precision quantization of vision models on edge CPUs. It evaluates 13 sensitivity metrics across four neural networks, finding that Jensen‑Shannon Divergence consistently identifies layers that can be safely quantized. Using K‑Means clustering on these metrics, RAMP achieves near‑lossless accuracy with an average 1.81× speed‑up, while cautioning against excluding low‑speed‑up layers that can fragment the computational graph.
arXiv Computer Vision
Oct 27
NanoVSR: Towards Real-Time Video Super-Resolution on Edge Devices
arXiv:2607.10495v2 Announce Type: replace Abstract: Recent Video Super-Resolution (VSR) methods rely heavily on transformers and explicit optical flow, creating computational overhead and custom oper...