Biblio

This chapter explores the relationship between the concept of emergence, the goal of theoretical completeness, and the Principle of Sufficient Reason. Samuel Alexander and C. D. Broad argued for limits to the power of scientific explanation. Chemical explanation played a central role in their thinking. After Schrödinger’s work in the 1920s their examples seem to fall flat. However, there are more general lessons from the emergentists that need to be explored. There are cases where we know that explanation of some phenomenon is impossible. What are the implications of known limits to the explanatory power of science, and the apparent ineliminability of brute facts for emergence? One lesson drawn here is that we must embrace a methodological rather than a metaphysical conception of the Principle of Sufficient Reason.

We present DeepPicar, a low-cost deep neural network based autonomous car platform. DeepPicar is a small scale replication of a real self-driving car called DAVE-2 by NVIDIA. DAVE-2 uses a deep convolutional neural network (CNN), which takes images from a front-facing camera as input and produces car steering angles as output. DeepPicar uses the same network architecture—9 layers, 27 million connections and 250K parameters—and can drive itself in real-time using a web camera and a Raspberry Pi 3 quad-core platform. Using DeepPicar, we analyze the Pi 3’s computing capabilities to support end-to-end deep learning based real-time control of autonomous vehicles. We also systematically compare other contemporary embedded computing platforms using the DeepPicar’s CNN-based real-time control workload. We find that all tested platforms, including the Pi 3, are capable of supporting the CNN-based real-time control, from 20 Hz up to 100 Hz, depending on hardware platform. However, we find that shared resource contention remains an important issue that must be considered in applying CNN models on shared memory based embedded computing platforms; we observe up to 11.6X execution time increase in the CNN based control loop due to shared resource contention. To protect the CNN workload, we also evaluate state-of-the-art cache partitioning and memory bandwidth throttling techniques on the Pi 3. We find that cache partitioning is ineffective, while memory bandwidth throttling is an effective solution.