Scaling and better approximating quantum Fourier transform by higher radices

Quantum Fourier Transform (QFT) plays a principal role in the development of efficient quantum algorithms. Since the number of quantum bits that can currently built is limited, while many quantum technologies are inherently three- (or more) valued, we consider extending the reach of the realistic quantum systems by building a QFT over ternary quantum digits. Compared to traditional binary QFT, the q-valued transform improves approximation properties and increases the state space by a factor of (q/2)n. Further, we use non-binary QFT derivation to generalize and improve the approximation bounds for QFT

Real Laboratories for Distance Education

Providing distance laboratory-based courses is becoming critical for distance technical education. In this work, we describe remote laboratories in digital system courses. While the hardware is based on widely used programmable logic, the Internet interfaces include those for remote development, testing and debugging as well as the cooperative work environment. Special attention has been paid to the objectivity of evaluating the remote cooperative work. The web tools for project progress evaluation, self- and group- assessment and the automated hardware support are being developed. Previous work consisted mainly of providing simulated environments or prefabricated circuits. The productivity and accessibility of these tools was greatly enhanced by using off-the-shelf hardware, software and networking elements

A Structurally Regularized CNN Architecture via Adaptive Subband Decomposition

We propose a generalized convolutional neural network (CNN) architecture that first decomposes the input signal into subbands by an adaptive filter bank structure, and then uses convolutional layers to extract features from each subband independently. Fully connected layers finally combine the extracted features to perform classification. The proposed architecture restrains each of the subband CNNs from learning using the entire input signal spectrum, resulting in structural regularization. Our proposed CNN architecture is fully compatible with the end-to-end learning mechanism of typical CNN architectures and learns the subband decomposition from the input dataset. We show that the proposed CNN architecture has attractive properties, such as robustness to input and weight-and-bias quantization noise, compared to regular full-band CNN architectures. Importantly, the proposed architecture significantly reduces computational costs, while maintaining state-of-the-art classification accuracy. Experiments on image classification tasks using the MNIST, CIFAR-10/100, Caltech-101, and ImageNet-2012 datasets show that the proposed architecture allows accuracy surpassing state-of-the-art results. On the ImageNet-2012 dataset, we achieved top-5 and top-1 validation set accuracy of 86.91% and 69.73%, respectively. Notably, the proposed architecture offers over 90% reduction in computation cost in the inference path and approximately 75% reduction in back-propagation (per iteration) with just a single-layer subband decomposition. With a 2-layer subband decomposition, the computational gains are even more significant with comparable accuracy results to the single-layer decomposition

Reliability aware NoC router architecture using input channel buffer sharing

To address the increasing demand for reliability in on-chip networks, we proposed a novel Reliability Aware Virtual channel (RAVC) NoC router micro-architecture that enables both dynamic virtual channel allocations and the rational sharing among the buffers of different input channels. In particular, in the case of failure in routers, the virtual channels of routers surrounding the faulty routers can be totally recaptured and reassigned to other input ports. Moreover, our proposed RAVC router isolates the faulty router from occupying network bandwidth. Experimental result shows that proposed micro-architecture provides 7.1 % and 3.1 % average latency decrease under uniform and transpose traffic pattern. Considering the existence of failures in routers of on-chip network, RAVC provides 28 % and 16 % decrease in the average packet latency under the uniform and transpose traffic pattern respectively

On the Efficient Design & Synthesis of Differential Clock Distribution Networks

Non

Multi-point Security by a Multiplatform-compatible Multifunctional Authentication and Encryption Board

Securing the access in networks is a first-order concern that only gains importance with the advent of Internet of Things (IoT). In this paper, a security system is presented for password-free access over the secured link. It makes the connection faster than manual authentication and facilitates Machine-to-Machine (M2M) secure interactions, as required for IoT. The authentication procedure includes the exchange of certificate and challenge/response pairs, which are stored and computed in an external security coprocessor. The system enforces the authentication protocol, includes error detection, and handles multiple devices according to their Operating Systems (OS) through their connections/ disconnections. It also performs encryption, if necessary. It is applicable on application level for devices, including IoT based devices, sensors, Android, and iOS-based smartphones. The devices that have the correct certificate and can solve the challenge can connect to the network linked with the security system. The system security is hardened because the sensitive authentication elements such as keys, certificates, and challenge responses are invisible to users and are exchanged only using strong hashing algorithms that are irreversible. The proposed hardware security system can augment any supporting network, converting the entire insecure network into a secured one, as well as retrofit existing insecure Bluetooth devices for secure access. The system incurs low overhead in time and energy by performing security operations in an ASIC coprocessor, and can be shared to secure access to multiple devices, which reduces both energy and cost