Efficient Sub-millisecond Latency Event-based Eye Tracking System with Submanifold Sparse CNN

Основные понятия

A hardware-software co-designed event-based eye tracking system that leverages submanifold sparse convolution neural networks to achieve sub-millisecond latency, low power consumption, and high precision.

Аннотация

The proposed eye tracking system, called SEE, utilizes an event-based camera and a novel hardware-software co-design approach to achieve outstanding performance in terms of low latency, low power consumption, and high precision.
The key components of the SEE system are:

Submanifold Sparse Convolution Neural Network (SCNN) Backbone:

The SCNN backbone efficiently extracts features from the sparse event-based input data by only processing the non-zero activations.
This helps preserve the inherent sparsity of the event data and reduces unnecessary computations.

Heterogeneous Hardware Architecture:

The SCNN backbone is implemented on an FPGA dataflow accelerator that can efficiently operate on sparse activations.
The recurrent and fully connected layers are executed on the Arm Cortex-A53 processor using SIMD instructions for efficient processing.
This heterogeneous design allows the system to fully exploit the strengths of different hardware components.

Software-Hardware Co-optimization:

A co-optimization framework is developed to search for compact network architectures that balance accuracy and hardware latency.
This ensures the entire model can fit within the on-chip memory of the FPGA, further improving efficiency.

The SEE system is extensively evaluated on the Event-based Eye-Tracking-AIS2024 dataset. The results demonstrate that the system can achieve over 98% p10 accuracy with 0.7 ms to 0.94 ms inference latency, while consuming only 2.29 mJ per inference. Compared to an embedded GPU, the SEE system achieves up to 15.4x and 77.1x speedup for standard and submanifold sparse convolution implementations, respectively.

Статистика

The system achieves 81% p5 accuracy, 99.5% p10 accuracy, and 3.71 Mean Euclidean Distance.
The system consumes only 2.29 mJ per inference.

Цитаты

"Notably, our solution opens up opportunities for future eye-tracking systems."
"Deployment and evaluation of our system reveal outstanding performance metrics."

Ключевые выводы из

Co-designing a Sub-millisecond Latency Event-based Eye Tracking System with Submanifold Sparse CNN

by Baoheng Zhan... в arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.14279.pdf

Дополнительные вопросы

How can the proposed hardware-software co-design approach be extended to other event-based vision tasks beyond eye tracking

The hardware-software co-design approach proposed in the context for event-based eye tracking can be extended to other event-based vision tasks by leveraging the same principles of efficiency, low latency, and power optimization. For tasks such as object detection, optical flow estimation, or action recognition using event-based cameras, a similar co-design strategy can be applied. By customizing the hardware architecture to suit the specific requirements of each task and optimizing the software algorithms to work seamlessly with the hardware accelerators, it is possible to achieve high performance across a range of event-based vision applications. Additionally, the co-optimization framework developed for model selection and hardware resource allocation can be adapted to different tasks by adjusting the search space and evaluation criteria based on the specific requirements of the task at hand.

What are the potential challenges and limitations of the submanifold sparse convolution technique, and how can they be addressed

The submanifold sparse convolution technique offers significant advantages in preserving spatial sparsity and reducing unnecessary computations, leading to improved efficiency in processing event-based data. However, there are potential challenges and limitations associated with this technique. One challenge is the increased complexity in managing the sparse data structures and coordinating the computations across different layers of the network. This complexity can lead to higher design and implementation overheads, especially when scaling the model to handle larger datasets or more complex tasks. To address this, advanced dataflow architectures and optimization techniques can be employed to streamline the processing pipeline and improve resource utilization.
Another limitation of submanifold sparse convolution is the potential trade-off between accuracy and efficiency. While the technique excels in reducing computational costs and latency, there may be instances where it sacrifices some level of accuracy compared to dense convolutional approaches. To mitigate this limitation, a careful balance between model complexity, sparsity patterns, and quantization strategies can be maintained. Fine-tuning the quantization parameters and exploring different network architectures can help optimize the trade-off between accuracy and efficiency in submanifold sparse convolution models.

Given the low-latency and energy-efficient nature of the SEE system, how could it be leveraged in real-world applications such as augmented reality or human-computer interaction

The SEE system, with its low-latency and energy-efficient design, holds significant potential for real-world applications such as augmented reality (AR) and human-computer interaction (HCI). In AR applications, the SEE system can enable seamless and responsive gaze tracking, enhancing user immersion and interaction with virtual elements. By integrating the SEE system into AR headsets or smart glasses, users can benefit from accurate and real-time eye tracking for intuitive control and interaction in AR environments.
In HCI applications, the SEE system can revolutionize the way users interact with computers and devices. By incorporating eye tracking capabilities into user interfaces, the SEE system can enable hands-free interaction, personalized user experiences, and adaptive interfaces based on gaze behavior. For example, in assistive technologies, the SEE system can facilitate gaze-based control for individuals with motor disabilities, offering a more accessible and efficient way to interact with devices.
Overall, the SEE system's capabilities can be leveraged to enhance user experiences, improve task efficiency, and enable new interaction paradigms in a wide range of real-world applications beyond eye tracking. Its low-latency performance and energy efficiency make it a valuable technology for driving innovation in AR, HCI, and other vision-based applications.