ETHEREAL: A 25.6-$μ$s/inf. Low-latency Event-driven Graph-neural-network Processor for High-resolution Vision at the Edge

• URL: http://arxiv.org/abs/2608.17787v1
• Date: Tue, 18 Aug 2026 13:49:50 GMT
• Title: ETHEREAL: A 25.6-$μ$s/inf. Low-latency Event-driven Graph-neural-network Processor for High-resolution Vision at the Edge
• Abstract summary: Event neural networks (EVGNNs) have emerged as a solution on the algorithmic side that is both accurate and efficient.<n>There is no dedicated hardware capable of efficiently supporting their mixed requirements of dense-regular compute operations and sparse-irregular memory accesses.<n>We introduce ETHEREAL, the first EV-GNN processor chip, capable of bridging this gap by means of a neighbor-parallel spline-con engine combined with a split-2D/3D memory.
• Score: 22.56098850302993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic vision sensors (DVS) are enticing candidates to reach the low-latency, sub-ms target of edge-vision applications, as they generate events with a $μ$s-level time resolution. However, using DVS front ends also calls for novel algorithm/hardware back ends capable of efficiently handling streams of sparse spatiotemporal events. While event-driven graph neural networks (EV-GNNs) have emerged as a solution on the algorithmic side that is both accurate and efficient, there is no dedicated hardware to date capable of efficiently supporting their mixed requirements of dense-regular compute operations and sparse-irregular memory accesses. We therefore introduce ETHEREAL, the first EV-GNN processor chip, capable of bridging this gap by means of a neighbor-parallel spline-convolution engine combined with a split-2D/3D memory hierarchy that introduces a novel spatiotemporal event-caching mechanism. Measurement results demonstrate a 25.6$μ$s latency and a 1.6$μ$J energy per end-to-end event-wise inference on the state-of-the art DAGr-GNN workload and VGA-resolution (640x480 pixels) DSEC dataset.

Related papers

• A 25-$μ$s/inf Event-driven Graph Neural Network Processor with Spatiotemporal Caching and Spline Convolution for Ultra-low-latency AI at the Edge [22.56098850302993]
  Event-driven graph neural networks (EV-GNNs) emerge as a promising algorithmic solution.<n>We present ETHEREAL, the first EV-GNN that scales to 640$times$480 resolutions.<n>Measurement results demonstrate end-to-end inference with 25.6$$s latency and 1.7$$ energy per event on state-of-the-art workloads.
  arXiv  Detail & Related papers  (2026-09-14T09:01:03Z)
• Embedded Graph Convolutional Networks for Real-Time Event Data Processing on SoC FPGAs [0.815557531820863]
  We introduce a custom EFGCN (Event-based FPGA-accelerated Graph Convolutional Network) designed with a series of hardware-aware optimisations tailored for PointNetConv.<n>The proposed techniques result in up to 100-fold reduction in model size compared to Asynchronous Event-based GNN (AEGNN)<n>Our approach achieves state-of-the-art performance across multiple event-based classification benchmarks while remaining highly scalable, customisable and resource-efficient.
  arXiv  Detail & Related papers  (2024-06-11T14:47:36Z)
• EvGNN: An Event-driven Graph Neural Network Accelerator for Edge Vision [0.06752396542927405]
  Event-driven graph neural networks (GNNs) have emerged as a promising solution for sparse event-based vision.<n>We propose EvGNN, the first event-driven GNN accelerator for low-footprint, ultra-low-latency, and high-accuracy edge vision.
  arXiv  Detail & Related papers  (2024-04-30T12:18:47Z)
• Ev-Edge: Efficient Execution of Event-based Vision Algorithms on Commodity Edge Platforms [10.104371980353973]
  Ev-Edge is a framework that contains three key optimizations to boost the performance of event-based vision systems on edge platforms. On several state-of-art networks for a range of autonomous navigation tasks, Ev-Edge achieves 1.28x-2.05x improvements in latency and 1.23x-2.15x in energy.
  arXiv  Detail & Related papers  (2024-03-23T04:44:55Z)
• Latency-aware Unified Dynamic Networks for Efficient Image Recognition [72.8951331472913]
  LAUDNet is a framework to bridge the theoretical and practical efficiency gap in dynamic networks. It integrates three primary dynamic paradigms-spatially adaptive computation, dynamic layer skipping, and dynamic channel skipping. It can notably reduce the latency of models like ResNet by over 50% on platforms such as V100,3090, and TX2 GPUs.
  arXiv  Detail & Related papers  (2023-08-30T10:57:41Z)
• Communication-Efficient Graph Neural Networks with Probabilistic Neighborhood Expansion Analysis and Caching [59.8522166385372]
  Training and inference with graph neural networks (GNNs) on massive graphs has been actively studied since the inception of GNNs. This paper is concerned with minibatch training and inference with GNNs that employ node-wise sampling in distributed settings. We present SALIENT++, which extends the prior state-of-the-art SALIENT system to work with partitioned feature data.
  arXiv  Detail & Related papers  (2023-05-04T21:04:01Z)
• HALSIE: Hybrid Approach to Learning Segmentation by Simultaneously Exploiting Image and Event Modalities [6.543272301133159]
  Event cameras detect changes in per-pixel intensity to generate asynchronous event streams. They offer great potential for accurate semantic map retrieval in real-time autonomous systems. Existing implementations for event segmentation suffer from sub-based performance. We propose hybrid end-to-end learning framework HALSIE to reduce inference cost by up to $20times$ versus art.
  arXiv  Detail & Related papers  (2022-11-19T17:09:50Z)
• Dynamic Split Computing for Efficient Deep Edge Intelligence [78.4233915447056]
  We introduce dynamic split computing, where the optimal split location is dynamically selected based on the state of the communication channel. We show that dynamic split computing achieves faster inference in edge computing environments where the data rate and server load vary over time.
  arXiv  Detail & Related papers  (2022-05-23T12:35:18Z)
• AEGNN: Asynchronous Event-based Graph Neural Networks [54.528926463775946]
  Event-based Graph Neural Networks generalize standard GNNs to process events as "evolving"-temporal graphs. AEGNNs are easily trained on synchronous inputs and can be converted to efficient, "asynchronous" networks at test time.
  arXiv  Detail & Related papers  (2022-03-31T16:21:12Z)
• EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference [82.1584439276834]
  Transformer-based language models such as BERT provide significant accuracy improvement for a multitude of natural language processing (NLP) tasks. We present EdgeBERT, an in-depth algorithm- hardware co-design for latency-aware energy optimization for multi-task NLP.
  arXiv  Detail & Related papers  (2020-11-28T19:21:47Z)
• PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning [57.20262984116752]
  We introduce a new dimension, fine-grained pruning patterns inside the coarse-grained structures, revealing a previously unknown point in design space. With the higher accuracy enabled by fine-grained pruning patterns, the unique insight is to use the compiler to re-gain and guarantee high hardware efficiency.
  arXiv  Detail & Related papers  (2020-01-01T04:52:07Z)

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.