Increasing the scalability of graph convolution for FPGA-implemented event-based vision

Event cameras are becoming increasingly popular as an alternative to traditional frame-based vision sensors, especially in mobile robotics. Taking full advantage of their high temporal resolution, high dynamic range, low power consumption and sparsity of event data, which only reflects changes in the observed scene, requires both an efficient algorithm and a specialised hardware platform. A recent trend involves using Graph Convolutional Neural Networks (GCNNs) implemented on a heterogeneous SoC FPGA. In this paper we focus on optimising hardware modules for graph convolution to allow flexible selection of the FPGA resource (BlockRAM, DSP and LUT) for their implementation. We propose a ''two-step convolution'' approach that utilises additional BRAM buffers in order to reduce up to 94% of LUT usage for multiplications. This method significantly improves the scalability of GCNNs, enabling the deployment of models with more layers, larger graphs sizes and their application for more dynamic scenarios.

Embedded Graph Convolutional Networks for Real-Time Event Data Processing on SoC FPGAs

The utilisation of event cameras represents an important and swiftly evolving trend aimed at addressing the constraints of traditional video systems. Particularly within the automotive domain, these cameras find significant relevance for their integration into embedded real-time systems due to lower latency and energy consumption. One effective approach to ensure the necessary throughput and latency for event processing systems is through the utilisation of graph convolutional networks (GCNs). In this study, we introduce a series of hardware-aware optimisations tailored for PointNet++, a GCN architecture designed for point cloud processing. The proposed techniques result in more than a 100-fold reduction in model size compared to Asynchronous Event-based GNN (AEGNN), one of the most recent works in the field, with a relatively small decrease in accuracy (2.3% for N-Caltech101 classification, 1.7% for N-Cars classification), thus following the TinyML trend. Based on software research, we designed a custom EFGCN (Event-Based FPGA-accelerated Graph Convolutional Network) and we implemented it on ZCU104 SoC FPGA platform, achieving a throughput of 13.3 million events per second (MEPS) and real-time partially asynchronous processing with a latency of 4.47 ms. We also address the scalability of the proposed hardware model to improve the obtained accuracy score. To the best of our knowledge, this study marks the first endeavour in accelerating PointNet++ networks on SoC FPGAs, as well as the first hardware architecture exploration of graph convolutional networks implementation for real-time continuous event data processing. We publish both software and hardware source code in an open repository: https://github.com/vision-agh/*** (will be published upon acceptance).

Optimising Graph Representation for Hardware Implementation of Graph Convolutional Networks for Event-based Vision

Event-based vision is an emerging research field involving processing data generated by Dynamic Vision Sensors (neuromorphic cameras). One of the latest proposals in this area are Graph Convolutional Networks (GCNs), which allow to process events in its original sparse form while maintaining high detection and classification performance. In this paper, we present the hardware implementation of a~graph generation process from an event camera data stream, taking into account both the advantages and limitations of FPGAs. We propose various ways to simplify the graph representation and use scaling and quantisation of values. We consider both undirected and directed graphs that enable the use of PointNet convolution. The results obtained show that by appropriately modifying the graph representation, it is possible to create a~hardware module for graph generation. Moreover, the proposed modifications have no significant impact on object detection performance, only 0.08% mAP less for the base model and the N-Caltech data set.Finally, we describe the proposed hardware architecture of the graph generation module.

Memory-Efficient Graph Convolutional Networks for Object Classification and Detection with Event Cameras

Recent advances in event camera research emphasize processing data in its original sparse form, which allows the use of its unique features such as high temporal resolution, high dynamic range, low latency, and resistance to image blur. One promising approach for analyzing event data is through graph convolutional networks (GCNs). However, current research in this domain primarily focuses on optimizing computational costs, neglecting the associated memory costs. In this paper, we consider both factors together in order to achieve satisfying results and relatively low model complexity. For this purpose, we performed a comparative analysis of different graph convolution operations, considering factors such as execution time, the number of trainable model parameters, data format requirements, and training outcomes. Our results show a 450-fold reduction in the number of parameters for the feature extraction module and a 4.5-fold reduction in the size of the data representation while maintaining a classification accuracy of 52.3%, which is 6.3% higher compared to the operation used in state-of-the-art approaches. To further evaluate performance, we implemented the object detection architecture and evaluated its performance on the N-Caltech101 dataset. The results showed an accuracy of 53.7 % mAP@0.5 and reached an execution rate of 82 graphs per second.

Traffic sign detection and recognition using event camera image reconstruction

This paper presents a method for detection and recognition of traffic signs based on information extracted from an event camera. The solution used a FireNet deep convolutional neural network to reconstruct events into greyscale frames. Two YOLOv4 network models were trained, one based on greyscale images and the other on colour images. The best result was achieved for the model trained on the basis of greyscale images, achieving an efficiency of 87.03%.