Abstract: This paper focuses on trends, opportunities and challenges of novel arithmetics for DNN signal processing, with particular reference to assisted and autonomous driving applications. Due to strict constrains in terms of latency, dependability and security of autonomous driving, machine perception (i.e. detection or decisions tasks) based on DNN can not be implemented relying on a remote cloud access. These tasks must be performed in real-time on embedded systems on-board the vehicle, particularly for the inference phase (considering the use of DNNs pre-trained during an off-line step). When developing a DNN computing platform, the choice of the computing arithmetics matters. Moreover, functional safe applications like autonomous driving pose severe constraints on the effect that signal processing accuracy has on final rate of wrong detection/decisions. Hence, after reviewing the different choices and trade-off concerning arithmetics, both in academia and industry, we highlight the issues in implementing DNN accelerators to achieve accurate and low-complex processing of automotive sensor signals (the latter coming from diverse sources like cameras, radars, lidars, ultrasonics). The focus is on both on general-purpose operations massively used in DNN like multiply, accumulation, compare, or on specific functions like for example sigmoid or hyperbolic tangent, used for neuron activation.
Keywords: Deep Neural Networks (DNN), AutonomousDriving, Real-Time Image & Signal Processing/Classification,Alternative Real Number Representations, HW Accelerators.
