Autonomous driving systems have attracted a signiicant amount of interest recently, and many industry leaders, such as Google, Uber, Tesla and Mobileye, have invested large amount of capital and engineering power on developing such systems. Building autonomous driving systems is particularly challenging due to stringent performance requirements in terms of both making the safe operational decisions and inishing processing at real-time. Despite the recent advancements in technology, such systems are still largely under experimentation and architecting end-to-end autonomous driving systems remains an open research question. To investigate this question, we irst present and formalize the design constraints for building an autonomous driving system in terms of performance, predictability, storage, thermal and power. We then build an end-to-end autonomous driving system using state-of-the-art award-winning algorithms to understand the design trade-ofs for building such systems. In our real-system characterization, we identify three computational bottlenecks, which conventional multicore CPUs are incapable of processing under the identiied design constraints. To meet these constraints, we accelerate these algorithms using three accelerator platforms including GPUs, FPGAs and ASICs, which can reduce the tail latency of the system by 169×, 10×, and 93× respectively. With accelerator-based designs, we are able to build an endto-end autonomous driving system that meets all the design constraints, and explore the trade-ofs among performance, power and the higher accuracy enabled by higher resolution cameras. CCS Concepts • Computer systems organization → Neural networks; Heterogeneous (hybrid) systems;

Datacenters, or warehouse scale computers, are rapidly increasing in size and power consumption. However, this growth comes at the cost of an increasing thermal load that must be removed to prevent overheating and server failure. In this paper, we propose to use phase changing materials (PCM) to shape the thermal load of a datacenter, absorbing and releasing heat when it is advantageous to do so. We present and validate a methodology to study the impact of PCM on a datacenter, and evaluate two important opportunities for cost savings. We find that in a datacenter with full cooling system subscription, PCM can reduce the necessary cooling system size by up to 12% without impacting peak throughput, or increase the number of servers by up to 14.6% without increasing the cooling load. In a thermally constrained setting, PCM can increase peak throughput up to 69% while delaying the onset of thermal limits by over 3 hours.