Jingyue Zhuge | Searcharxiv

arXiv2026

A Low-Power Sparse Convolution Accelerator with Idle-First-Task-Assignment for Edge Vision

In recent years, edge-vision monitoring systems for applications such as smart animal husbandry have faced strict tripartite constraints: maintaining input resolution under extremely limited transmission bandwidth and strict power budgets. Conventional dense convolutional neural networks (CNNs) cannot satisfy the resource limits of such constrained IoT nodes. To address this challenge, this paper presents a low-power sparse convolution accelerator for edge devices, fabricated and validated in a 16 nm process. First, the accelerator adopts a bitmap-based format for compression in both data transmission and computation, effectively reducing memory and bandwidth overhead. Second, to mitigate load imbalance in sparse computation, an Idle-First-Task-Assignment (IFTA) dynamic scheduling strategy is proposed, significantly reducing processing-element (PE) idle time and improving multiplier utilization. In addition, a dedicated dataflow is designed to support and accelerate depthwise separable convolution (DWConv), which is widely used in lightweight networks. Experimental results show that the chip occupies only 0.5~mm$^2$ core area and consumes as little as 12--16~mW. On ImageNet, for sparse VGG16 and MobileNetV2, the proposed accelerator achieves 6.5$\times$ and 2.8$\times$ speedups, respectively, over traditional dense accelerators, and also delivers significant performance gains over the existing sparse accelerator.

cs.AR↗

arXiv2024

Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models

Event-based sensors are well suited for real-time processing due to their fast response times and encoding of the sensory data as successive temporal differences. These and other valuable properties, such as a high dynamic range, are suppressed when the data is converted to a frame-based format. However, most current methods either collapse events into frames or cannot scale up when processing the event data directly event-by-event. In this work, we address the key challenges of scaling up event-by-event modeling of the long event streams emitted by such sensors, which is a particularly relevant problem for neuromorphic computing. While prior methods can process up to a few thousand time steps, our model, based on modern recurrent deep state-space models, scales to event streams of millions of events for both training and inference. We leverage their stable parameterization for learning long-range dependencies, parallelizability along the sequence dimension, and their ability to integrate asynchronous events effectively to scale them up to long event streams. We further augment these with novel event-centric techniques enabling our model to match or beat the state-of-the-art performance on several event stream benchmarks. In the Spiking Speech Commands task, we improve state-of-the-art by a large margin of 7.7% to 88.4%. On the DVS128-Gestures dataset, we achieve competitive results without using frames or convolutional neural networks. Our work demonstrates, for the first time, that it is possible to use fully event-based processing with purely recurrent networks to achieve state-of-the-art task performance in several event-based benchmarks.

cs.LG↗