February 17th, 2017

Nonvolatile memories are being used for quite long time in computer systems. So far, they successfully added another level of abstraction to the current memory hierarchy. The introduction of byte addressable nonvolatile memory may end-up serving the same purpose; i.e., yet another technology evolution, or may be used to carry on the next revolution in system architectures. In his talk, he will give a short survey of what had been done so far in this area and will discuss few challenges that still need to be solved in order to enable NVDRAM to become the enabler of the next computer architecture revolution

Towards Open Scan for the Open-source Hardware

The open-source hardware IP model has recently started gaining popularity in the developer community. This model offers the integrated circuit (IC) developers wider standardization, faster time-to-market and richer platform for research. In addition, open-source hardware conforms to the Kerckhoff’s principle of a publicly-known algorithm and thus helps to enhance security. However, when security comes into consideration, source transparency is only one part of the solution. A complex global IC supply chain stands between the source and the final product. Hence, even if the source is known, the finished product is not guaranteed to match it. In this article, we propose the Open Scan model, in which, in addition to the source code, the IC vendor contributes a library-independent information on scan insertion. With scan information available, the user or a certification lab can perform partial reverse engineering of the IC to verify conformance to the advertised source. Compliance lists of open-source programs, such as of the OpenTitan cryptographic IC, can be amended to include this requirement. The Open Scan model addresses accidental and dishonest deviations from the golden model and partially addresses malicious modifications, known as hardware Trojans. We verify the efficiency of the proposed method in simulation with the Trust-Hub Trojan benchmarks and with several open-source benchmarks, in which we randomly insert modifications

Bimodal Distributed Binarized Neural Networks

Binary Neural Networks (BNNs) are an extremely promising method to reduce deep neural networks' complexity and power consumption massively. Binarization techniques, however, suffer from ineligible performance degradation compared to their full-precision counterparts. Prior work mainly focused on strategies for sign function approximation during forward and backward phases to reduce the quantization error during the binarization process. In this work, we propose a Bi-Modal Distributed binarization method (\methodname{}). That imposes bi-modal distribution of the network weights by kurtosis regularization. The proposed method consists of a training scheme that we call Weight Distribution Mimicking (WDM), which efficiently imitates the full-precision network weight distribution to their binary counterpart. Preserving this distribution during binarization-aware training creates robust and informative binary feature maps and significantly reduces the generalization error of the BNN. Extensive evaluations on CIFAR-10 and ImageNet demonstrate the superiority of our method over current state-of-the-art schemes. Our source code, experimental settings, training logs, and binary models are available at \url{https://github.com/BlueAnon/BD-BNN}