LiteEvent-AE: Lightweight Autoencoder for Event-Based Vision on Low-Latency Energy-Constrained Edge Devices

Riadul Islam; Joey Mule; Dhandeep Challagundla; Shahmir Rizvi; Sean Carson; Rachit Saini

cs.CVAug 22, 2026

LiteEvent-AE: Lightweight Autoencoder for Event-Based Vision on Low-Latency Energy-Constrained Edge Devices

Authors: Riadul Islam, Joey Mule, Dhandeep Challagundla, Shahmir Rizvi, Sean Carson, Rachit Saini

Organizations: Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, MD 21250, USA

Abstract

Event-based vision has emerged as a promising paradigm for energy-aware artificial intelligence (AI), offering sparse, low-latency visual signals that reduce redundant data processing and support sustainable edge computing. However, the asynchronous and noise-prone nature of event streams creates challenges for conventional deep learning models, which are often too computationally intensive for low-power embedded platforms. This work presents a compact and configurable event-driven autoencoder that efficiently compresses neuromorphic data while preserving essential spatiotemporal structure for downstream inference. The architecture integrates lightweight convolutional encoding with robust performance under adaptive event thresholding and a minimal classifier head, enabling substantial reductions in computational cost without degrading recognition fidelity. Extensive evaluations on the Smart Event Face Dataset (SEFD) and Event-Based Crossing Dataset (EBCD) show that the proposed framework achieves competitive or superior accuracy compared to YOLOv9 while requiring up to 35.6 $\times$ fewer parameters. To assess real-world sustainability, the model is deployed on resource-constrained hardware: a Raspberry Pi 4B and a NVIDIA Jetson Nano. On NVIDIA Jetson Nano, it delivers real-time throughput of 44.8 FPS. On a Raspberry Pi 4B CPU, the 50% autoencoder classifier consumes 16.19 J for the evaluated inference workload, corresponding to approximately 726.3 $\times$ lower energy consumption than YOLOv9 under the same evaluation protocol. These results demonstrate the potential of compact event-driven models to advance environmentally conscious, low-power AI systems for high-speed perception in autonomous, mobile, and embedded computing environments.

Figures & tables

Fig. 1: Comparison of average model accuracy, latency, and energy consumption on the SEFD [ 28 ] dataset, evaluated on a Raspberry Pi 4B CPU.

Fig. 2: (a) The proposed event autoencoder uses a conventional convolution layer followed by ReLU and Max pooling layer on the encoder side, while the decoder uses a similar convolution layer followed by ReLU but an up-sampling layer; and (b) the autoencoder-based classifier concatenates the encoder with two fully connected layers.

Fig. 3: Comprehensive edge-computing testbed integrating Raspberry Pi 4B and NVIDIA Jetson Nano boards with a Raspberry Pi camera and USB-based power analysis tools, enabling systematic evaluation of computational load, energy consumption, and real-time performance.

Model	Params	$T_{h}$	Accuracy	Precision	Recall	F1-Score	FLOPs
YOLOv4 [ 6 ]	60.3M	4	83.81	94.00	89.00	91.00	59.56B
		8	80.33	89.00	89.00	89.00
		12	79.98	91.00	86.00	89.00
		16	74.49	93.00	79.00	85.00
YOLOv7 [ 60 ]	25.2M	4	86.71	94.00	92.00	93.00	43.61B
		8	82.95	92.00	90.00	91.00

TABLE I: Performance comparison on the SEFD [ 28 ] test dataset under temporal thresholds

T_{h}\in{\{4,8,12,16\}}

. Our autoencoder classifier achieves competitive performance on frame-level data with orders of magnitude fewer parameters than heavyweight baselines.

Model	Parameters	$T_{h}$	Accuracy	Precision	Recall	F1-Score	FLOPs
YOLOv4 [ 6 ]	60.3M	12	88.00	96.00	80.00	87.00	59.56B
		16	87.50	94.00	81.00	87.00
		20	89.00	94.00	84.00	87.00
YOLOv7 [ 60 ]	25.2M	12	77.99	97.00	78.00	87.00	43.61B
		16	77.99	97.00	78.00	86.00
		20	79.35	97.00	79.00	87.00

TABLE II: Performance comparison on the EBCD [ 44 ] test dataset

Fig. 4: (a) Training curve of the full-capacity event autoencoder, yielding an average loss close to fifteen hundredths. (b) Performance of the reduced 50%–filter configuration, which achieves a lower average training loss close to one-tenth, illustrating the model’s resilience under substantial architectural compression.

Fig. 5: The reconstruction outputs of the proposed models exhibit excellent accuracy, comparable to their model sizes.

Fig. 6: AUROC performance of the proposed autoencoder-based classifier across SEFD threshold settings. All configurations achieve AUC values above 90%, with peak performance at $T_{h}=8$

Fig. 7: Runtime evaluation of the proposed architectures compared with contemporary work on Raspberry Pi 4B and NVIDIA Jetson Nano (4GB). The results show reduced execution time and higher FPS, with the autoencoder classifier achieving 22.1 $\times$ the FPS of YOLOv9. Although YuNet is fastest, the proposed 50% filter classifier is 6.3% more accurate.

Fig. 8: Energy consumption and runtime during inference on a Raspberry Pi 4B. The proposed 50% Autoencoder Classifier and 50% Autoencoder require only 16.19 J and 30.48 J to process 100 images, achieving more than a $2\times$ energy reduction over their full-size counterparts (36.37 J and 64.46 J). Their runtimes are similarly low at 4.8 s and 8.6 s. In contrast, lightweight baselines such as MobileNet-v1 and YOLOv7 consume 3234.95 J and 8440.31 J (476.2 s and 1250.0 s), while the most demanding model, YOLOv9, reaches 11,759.39 J and 2000.0 s—over $700\times$ the energy usage of the 50% Autoencoder Classifier.

Model	Parameters	$T_{h}$	Accuracy	Precision	Recall	F1-Score	FLOPs
Autoencoder Classifier 25% (Ours)	132K	4	88.79	86.48	82.97	84.69	429M
		8	86.30	86.33	86.25	86.29
		12	86.46	85.11	88.39	86.71
		16	84.56	82.32	88.02	85.07

TABLE III: Ablation on the analysis of a further reduced autoencoder classifier, evaluated on the SEFD [ 28 ] dataset across temporal thresholds

T_{h}\in\{4,8,12,16\}

Event-Based Vision

Event-based vision uses asynchronous camera measurements to perceive motion and scene changes with high temporal resolution and dynamic range.

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Lightweight Multi-Scale Anomaly Detection for Resource-Constrained Edge Devices

Raheen Junaid Wani, Smruti R. Sarangi

School of Information Technology Indian Institute of Technology, Delhi New Delhi, India 110016 · Department of Computer Science Indian Institute of Technology, Delhi New Delhi, India 110016

Autoencoders Edge Inference Reconstruction-Based Anomaly Detection Time-Series Anomaly Detection Multiscale Time-Series Modeling

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Time-series anomaly detection is increasingly important in IoT systems, sensor networks, and edge monitoring applications, where models must operate under strict constraints on memory, latency, and power consumption. While recent deep-learning approaches have improved detection accuracy, many remain computationally expensive and often fail to capture subtle anomalies due to limited multi-scale sensitivity. Autoencoders are widely used for anomaly detection because they reconstruct normal patterns well, leading to elevated reconstruction errors for anomalous inputs. Their simplicity and efficiency also make them suitable lightweight backbones for handling multi-scale inputs. To address these challenges, we propose a Lightweight MultiScale AutoEncoder (LMSAE) network for univariate time-series anomaly detection, designed to be compact and computationally efficient. LMSAE leverages the Discrete Wavelet Transform (DWT) to extract multi-scale features and employs a multi-scale loss function to improve sensitivity to subtle or hidden anomalies. Experiments on benchmark datasets demonstrate competitive or superior detection performance despite using significantly fewer parameters and a model size of less than 500 KB. LMSAE also achieves low-latency, low-power inference on the NVIDIA Jetson Nano, with 9x reduction in inference latency and 2x reduction in power consumption, making it ideal for edge deployment.

Abstract

Event-Based Vision

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