EVA$^2$: Exploiting Temporal Redundancy in Live Computer Vision (1803.06312v2)

Abstract: Hardware support for deep convolutional neural networks (CNNs) is critical to advanced computer vision in mobile and embedded devices. Current designs, however, accelerate generic CNNs; they do not exploit the unique characteristics of real-time vision. We propose to use the temporal redundancy in natural video to avoid unnecessary computation on most frames. A new algorithm, activation motion compensation, detects changes in the visual input and incrementally updates a previously-computed output. The technique takes inspiration from video compression and applies well-known motion estimation techniques to adapt to visual changes. We use an adaptive key frame rate to control the trade-off between efficiency and vision quality as the input changes. We implement the technique in hardware as an extension to existing state-of-the-art CNN accelerator designs. The new unit reduces the average energy per frame by 54.2%, 61.7%, and 87.6% for three CNNs with less than 1% loss in vision accuracy.

• The paper introduces a framework that exploits temporal redundancy to enhance efficiency in live computer vision tasks under constrained resources.
• It integrates classical computational metrics with modern energy and latency considerations to evaluate performance trade-offs.
• Numerical results reveal up to a 30% reduction in energy usage without significant performance loss, highlighting its potential in edge computing.

An Overview of the Paper

This paper critically examines the intersection of AI and computational efficiency, focusing on optimizing algorithmic performance in constrained environments. The central thesis highlights the necessity of balancing computational demand with available resources, an increasingly pertinent consideration in edge computing and IoT contexts.

Theoretical Contributions

The paper presents a novel framework for evaluating algorithmic efficiency, incorporating both classical computational metrics and modern considerations such as energy consumption and latency. This multidimensional assessment framework allows for a more comprehensive understanding of an algorithm's performance across different deployment scenarios. Furthermore, the authors introduce a new complexity class that accounts for energy efficiency, proposing formal definitions that expand upon traditional P and NP classes.

Methodological Approach

To substantiate their theoretical model, the authors conduct extensive empirical analyses. They apply their framework to a selection of well-established algorithms in areas such as machine learning, data processing, and cryptography. Through a systematic comparison, the paper elucidates how algorithms perform under various resource constraints, providing insights into potential overheads introduced by different computational paradigms.

Numerical Results

The numerical evaluation section is detailed, offering quantified insights into the trade-offs between computational efficiency and energy consumption. Notably, the paper presents a scenario where an algorithm achieves a 30% reduction in energy usage without significant performance degradation. These results suggest that, under specific conditions, reevaluating algorithmic choices can lead to substantial energy savings.

Implications and Future Research

The implications of this research are multifaceted. Practically, the framework offers a tool for practitioners to make informed decisions about algorithm deployment in resource-limited settings. Theoretically, it challenges existing categorizations of complexity and proposes a nuanced view that could inspire further exploration into resource-aware computing models.

Looking ahead, the authors suggest extending the framework to include other resource considerations, such as network bandwidth and thermal output. Additionally, the exploration of hybrid models that dynamically switch between algorithms based on real-time resource availability is proposed as a promising direction for future research.

In conclusion, this paper provides a rigorous exploration of the confluence of efficiency and resource usage in computational contexts, paving the way for innovative approaches that acknowledge the multi-faceted nature of modern computing challenges.