Securing deep neural networks (DNNs) is a problem of significant interest since an ML model incorporates high-quality intellectual property, features of data sets painstakingly collated by mechanical turks, and novel methods of training on large cluster computers. Sadly, attacks to extract model parameters are on the rise, and thus designers are being forced to create architectures for securing such models. State-of-the-art proposals in this field take the deterministic memory access patterns of such networks into cognizance (albeit partially), group a set of memory blocks into a tile, and maintain state at the level of tiles (to reduce storage space). For providing integrity guarantees (tamper avoidance), they don’t propose any significant optimizations, and still maintain block-level state.We observe that it is possible to exploit the deterministic memory access patterns of DNNs even further, and maintain state information for only the current tile and current layer, which may comprise a large number of tiles. This reduces the storage space, reduces the number of memory accesses, increases performance, and simplifies the design without sacrificing any security guarantees. The key techniques in our proposed accelerator architecture, Securator, are to encode memory access patterns to create a small HW-based tile version number generator for a given layer, and to store layer-level MACs. We completely eliminate the need for having a MAC cache and a tile version number store (as used in related work). We show that using intelligently-designed mathematical operations, these structures are not required. By reducing such overheads, we show a speedup of 20.56% over the closest competing work.

Stereo vision algorithms are important building blocks of self-driving applications. The two primary requirements of a self-driving vehicle are real-time operation and nearly 100% accuracy in constructing the 3D scene regardless of the weather conditions and the degree of ambient light. Sadly, most real-time systems as of today provide a level of accuracy that is inadequate and this endangers the life of the passengers; consequently, it is necessary to supplement such systems with expensive LiDAR-based sensors. We observe that for a given scene, different stereo matching algorithms can have vastly different accuracies, and among these algorithms there is no clear winner. This makes the case for a hybrid stereo vision system where the best stereo vision algorithm for a stereo image pair is chosen by a predictor dynamically, in real-time.We implement such a system called PredStereo in ASIC1that combines two diametrically different stereo vision algorithms, CNN-based and traditional, and chooses the best one at runtime. In addition, it associates a confidence with the chosen algorithm, such that the higher-level control system can be switched on in case of a low confidence value. We show that designing a predictor that is explainable and a system that respects soft real-time constraints is non-trivial. Hence, we propose a variety of hardware optimizations that enable our system to work in real-time. Overall, PredStereo improves the disparity estimation error over a state-of-the-art CNN-based stereo vision system by up to 18% (on average 6.25%) with a negligible area overhead (0.003mm2) while respecting real-time constraints.