Event cameras have emerged as a promising sensing modality for autonomous
navigation systems, owing to their high temporal resolution, high dynamic range
and negligible motion blur. To process the asynchronous temporal event streams
from such sensors, recent research has shown that a mix of Artificial Neural
Networks (ANNs), Spiking Neural Networks (SNNs) as well as hybrid SNN-ANN
algorithms are necessary to achieve high accuracies across a range of
perception tasks. However, we observe that executing such workloads on
commodity edge platforms which feature heterogeneous processing elements such
as CPUs, GPUs and neural accelerators results in inferior performance. This is
due to the mismatch between the irregular nature of event streams and diverse
characteristics of algorithms on the one hand and the underlying hardware
platform on the other. We propose Ev-Edge, a framework that contains three key
optimizations to boost the performance of event-based vision systems on edge
platforms: (1) An Event2Sparse Frame converter directly transforms raw event
streams into sparse frames, enabling the use of sparse libraries with minimal
encoding overheads (2) A Dynamic Sparse Frame Aggregator merges sparse frames
at runtime by trading off the temporal granularity of events and computational
demand thereby improving hardware utilization (3) A Network Mapper maps
concurrently executing tasks to different processing elements while also
selecting layer precision by considering both compute and communication
overheads. On several state-of-art networks for a range of autonomous
navigation tasks, Ev-Edge achieves 1.28x-2.05x improvements in latency and
1.23x-2.15x in energy over an all-GPU implementation on the NVIDIA Jetson
Xavier AGX platform for single-task execution scenarios. Ev-Edge also achieves
1.43x-1.81x latency improvements over round-robin scheduling methods in
multi-task execution scenarios.

Ev-Edge 是一个框架，通过三项关键优化技术提高边缘平台上基于事件的视觉系统的性能，包括将事件流转换为稀疏帧、动态稀疏帧聚合和网络映射。在多种自主导航任务中，Ev-Edge 相较于 NVIDIA Jetson Xavier AGX 平台上全 GPU 实现的单任务执行中具有 1.28 倍至 2.05 倍的延迟改进和 1.23 倍至 2.15 倍的能源改进，也相较于循环调度方法在多任务执行场景下具有 1.43 倍至 1.81 倍的延迟改进。

Ev-Edge: 基于事件的视觉算法在通用边缘平台上的高效执行

Ev-Edge: Efficient Execution of Event-based Vision Algorithms on  Commodity Edge Platforms

Deploying Convolutional Neural Networks (CNNs) on edge platforms necessitates
efficient hardware acceleration. Any unnecessary data movement in such
accelerators can unacceptably degrade performance and efficiency. To address
this, we develop a layer fusion technique targeting CNNs, that reduces off-chip
data communication using a Genetic Algorithm (GA) applied to graph-based
topological sort. Results show a 1.8$\times$ increase in energy efficiency and
1.9$\times$ improvement in energy-delay product (EDP) for MobileNet-v3 on a
SIMBA-like mobile architecture. Our approach consistently improves workload
performance, averaging 1.4$\times$ improvement to EDP for SIMBA and
1.12$\times$ for Eyeriss.

通过图形排序的遗传算法，我们开发了一种针对卷积神经网络的层融合技术，减少了边缘平台上的数据传输，从而提高了能效和能延迟乘积（EDP），在类似 SIMBA 移动架构上的 MobileNet-v3 中实现了 1.8 倍的能效提升和 1.9 倍的 EDP 改善。我们的方法对 SIMBA 平台和 Eyeriss 平台的工作负载性能一致改善，平均 EDP 改善 1.4 倍和 1.12 倍。

使用遗传算法改进 CNN 加速器的层间流水线

Improvements in Interlayer Pipelining of CNN Accelerators Using Genetic  Algorithms

Lifelong learning - an agent's ability to learn throughout its lifetime - is
a hallmark of biological learning systems and a central challenge for
artificial intelligence (AI). The development of lifelong learning algorithms
could lead to a range of novel AI applications, but this will also require the
development of appropriate hardware accelerators, particularly if the models
are to be deployed on edge platforms, which have strict size, weight, and power
constraints. Here, we explore the design of lifelong learning AI accelerators
that are intended for deployment in untethered environments. We identify key
desirable capabilities for lifelong learning accelerators and highlight metrics
to evaluate such accelerators. We then discuss current edge AI accelerators and
explore the future design of lifelong learning accelerators, considering the
role that different emerging technologies could play.

生命周期学习的发展需要适合边缘平台的硬件加速器，该研究探讨了如何设计面向无线环境部署的生命周期学习 AI 加速器，以及评估这些加速器的关键功能和指标，并探讨了不同新兴技术在生命周期学习加速器设计中的作用。