mlsanityData to 14 Oct 2026

Memory-Oriented Design-Space Exploration of Edge-AI Hardware for XR Applications

arXiv 2206.06780 ↗First posted 8 Jun 2022cs.AR cs.AIcited by 1

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Abstract (arXiv)

Low-Power Edge-AI capabilities are essential for on-device extended reality (XR) applications to support the vision of Metaverse. In this work, we investigate two representative XR workloads: (i) Hand detection and (ii) Eye segmentation, for hardware design space exploration. For both applications, we train deep neural networks and analyze the impact of quantization and hardware specific bottlenecks. Through simulations, we evaluate a CPU and two systolic inference accelerator implementations. Next, we compare these hardware solutions with advanced technology nodes. The impact of integrating state-of-the-art emerging non-volatile memory technology (STT/SOT/VGSOT MRAM) into the XR-AI inference pipeline is evaluated. We found that significant energy benefits (>=24%) can be achieved for hand detection (IPS=10) and eye segmentation (IPS=0.1) by introducing non-volatile memory in the memory hierarchy for designs at 7nm node while meeting minimum IPS (inference per second). Moreover, we can realize substantial reduction in area (>=30%) owing to the small form factor of MRAM compared to traditional SRAM.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 21 references that are in the map. First posted on arXiv 8 Jun 2022.

Builds on

8 of 21 references.

References · 8

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  3. 2017First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotationscited by 230
  4. 2020Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectorscited by 80
  5. 2021DeepScaleTool : A Tool for the Accurate Estimation of Technology Scaling in the Deep-Submicron Eracited by 30
  6. 2019OpenEDS: Open Eye Datasetcited by 27
  7. 2022Real-Time Gaze Tracking with Event-Driven Eye Segmentationcited by 21
  8. 2020Exploring Extended Reality with ILLIXR: A New Playground for Architecture Researchcited by 2

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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Datacited by 1

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  1. 2021Edge AI without Compromise: Efficient, Versatile and Accurate Neurocomputing in Resistive Random-Access Memory
  2. 2025Mamba-X: An End-to-End Vision Mamba Accelerator for Edge Computing Devices
  3. 2026Seeing is Free, Speaking is Not: Uncovering the True Energy Bottleneck in Edge VLM Inference
  4. 2026On the Limitations of Non-GPU AI Accelerators for Large-Model Inference: A Field Study of MoE and Multimodal Serving on Huawei Ascend
  5. 2026Seeing at the Edge: Energy Characterization of Vision-Language Model Inference on Edge Platforms
  6. 2026STEEL: Sparsity-Aware Fused Attention for Energy-Efficient Long-Sequence Inference on AMD's XDNA NPU
  7. 2026Deep Reinforcement Learning-driven Edge Offloading for Latency-constrained XR pipelines
  8. 2021DeepEdgeBench: Benchmarking Deep Neural Networks on Edge Devices