CAD Tools for Synthesis of Sleep Convention Logic

This dissertation proposes an automated flow for the Sleep Convention Logic (SCL) asynchronous design style. The proposed flow synthesizes synchronous RTL into an SCL netlist. The flow utilizes commercial design tools, while supplementing missing functionality using custom tools. A method for determining the performance bottleneck in an SCL design is proposed. A constraint-driven method to increase the performance of linear SCL pipelines is proposed. Several enhancements to SCL are proposed, including techniques to reduce the number of registers and total sleep capacitance in an SCL design

Economic Networks in the Laboratory: A Survey

This paper provides a survey of recent experimental work in economics focusing on social and economic networks. The experiments consider networks of coordination and cooperation, buyer-seller networks, and network formatio

Learning to predict closed questions on stack overflow

The paper deals with the problem of predicting whether the user’s question will be closed by the moderator on Stack Overflow, a popular question answering service devoted to software programming. The task along with data and evaluation metrics was offered as an open machine learning competition on Kaggle platform. To solve this problem, we employed a wide range of classification features related to users, their interactions, and post content. Classification was carried out using several machine learning methods. According to the results of the experiment, the most important features are characteristics of the user and topical features of the question. The best results were obtained using Vowpal Wabbit – an implementation of online learning based on stochastic gradient descent. Our results are among the best ones in overall ranking, although they were obtained after the official competition was over

Formulating a Strategy for Securing High-Speed Rail in the United States, Research Report 12-03

This report presents an analysis of information relating to attacks, attempted attacks, and plots against high-speed rail (HSR) systems. It draws upon empirical data from MTI’s Database of Terrorist and Serious Criminal Attacks Against Public Surface Transportation and from reviews of selected HSR systems, including onsite observations. The report also examines the history of safety accidents and other HSR incidents that resulted in fatalities, injuries, or extensive asset damage to examine the inherent vulnerabilities (and strengths) of HSR systems and how these might affect the consequences of terrorist attacks. The study is divided into three parts: (1) an examination of security principles and measures; (2) an empirical examination of 33 attacks against HSR targets and a comparison of attacks against HSR targets with those against non-HSR targets; and (3) an examination of 73 safety incidents on 12 HRS systems. The purpose of this study is to develop an overall strategy for HSR security and to identify measures that could be applied to HSR systems currently under development in the United States. It is hoped that the report will provide useful guidance to both governmental authorities and transportation operators of current and future HSR systems

Hyperdrive: A Multi-Chip Systolically Scalable Binary-Weight CNN Inference Engine

Deep neural networks have achieved impressive results in computer vision and machine learning. Unfortunately, state-of-the-art networks are extremely compute and memory intensive which makes them unsuitable for mW-devices such as IoT end-nodes. Aggressive quantization of these networks dramatically reduces the computation and memory footprint. Binary-weight neural networks (BWNs) follow this trend, pushing weight quantization to the limit. Hardware accelerators for BWNs presented up to now have focused on core efficiency, disregarding I/O bandwidth and system-level efficiency that are crucial for deployment of accelerators in ultra-low power devices. We present Hyperdrive: a BWN accelerator dramatically reducing the I/O bandwidth exploiting a novel binary-weight streaming approach, which can be used for arbitrarily sized convolutional neural network architecture and input resolution by exploiting the natural scalability of the compute units both at chip-level and system-level by arranging Hyperdrive chips systolically in a 2D mesh while processing the entire feature map together in parallel. Hyperdrive achieves 4.3 TOp/s/W system-level efficiency (i.e., including I/Os)---3.1x higher than state-of-the-art BWN accelerators, even if its core uses resource-intensive FP16 arithmetic for increased robustness

A comparative analysis of parallel processing and super-individual methods for improving the computational performance of a large individual-based model

Individual-based modelling approaches are being used to simulate larger complex spatial systems in ecology and in other fields of research. Several novel model development issues now face researchers: in particular how to simulate large numbers of individuals with high levels of complexity, given finite computing resources. A case study of a spatially-explicit simulation of aphid population dynamics was used to assess two strategies for coping with a large number of individuals: the use of ‘super-individuals’ and parallel computing. Parallelisation of the model maintained the model structure and thus the simulation results were comparable to the original model. However, the super-individual implementation of the model caused significant changes to the model dynamics, both spatially and temporally. When super-individuals represented more than around 10 individuals it became evident that aggregate statistics generated from a super-individual model can hide more detailed deviations from an individual-level model. Improvements in memory use and model speed were perceived with both approaches. For the parallel approach, significant speed-up was only achieved when more than five processors were used and memory availability was only increased once five or more processors were used. The super-individual approach has potential to improve model speed and memory use dramatically, however this paper cautions the use of this approach for a density-dependent spatially-explicit model, unless individual variability is better taken into account