Learning enabled components (LECs), while critical for decision making in
autonomous vehicles (AVs), are likely to make incorrect decisions when
presented with samples outside of their training distributions.
Out-of-distribution (OOD) detectors have been proposed to detect such samples,
thereby acting as a safety monitor, however, both OOD detectors and LECs
require heavy utilization of embedded hardware typically found in AVs. For both
components, there is a tradeoff between non-functional and functional
performance, and both impact a vehicle's safety. For instance, giving an OOD
detector a longer response time can increase its accuracy at the expense of the
LEC. We consider an LEC with binary output like an autonomous emergency braking
system (AEBS) and use risk, the combination of severity and occurrence of a
failure, to model the effect of both components' design parameters on each
other's functional and non-functional performance, as well as their impact on
system safety. We formulate a co-design methodology that uses this risk model
to find the design parameters for an OOD detector and LEC that decrease risk
below that of the baseline system and demonstrate it on a vision based AEBS.
Using our methodology, we achieve a 42.3% risk reduction while maintaining
equivalent resource utilization.

使用风险模型来找到能减少基线系统风险的 OOD 探测器和 LEC 的设计参数的协同设计方法，并应用于基于视觉的 AEBS，成功实现了 42.3% 的风险降低并保持等效资源利用。

自动紧急制动系统的区别分辨设计

Co-Design of Out-of-Distribution Detectors for Autonomous Emergency  Braking Systems

The rapidly growing demands for powerful AI algorithms in many application
domains have motivated massive investment in both high-quality deep neural
network (DNN) models and high-efficiency implementations. In this position
paper, we argue that a simultaneous DNN/implementation co-design methodology,
named Neural Architecture and Implementation Search (NAIS), deserves more
research attention to boost the development productivity and efficiency of both
DNN models and implementation optimization. We propose a stylized design
methodology that can drastically cut down the search cost while preserving the
quality of the end solution.As an illustration, we discuss this
DNN/implementation methodology in the context of both FPGAs and GPUs. We take
autonomous driving as a key use case as it is one of the most demanding areas
for high quality AI algorithms and accelerators. We discuss how such a
co-design methodology can impact the autonomous driving industry significantly.
We identify several research opportunities in this exciting domain.

本论文提出一种名为 “神经结构和实现搜索（NAIS）” 的设计方法，该方法应用于深度神经网络 (DNN) 和实现优化方面，以提高开发生产率和效率。同时，论文还讨论了该方法在 FPGA 和 GPU 上的应用以及在自动驾驶领域的研究机会。