Publications

Description

The paper contains a counter measure against logical side channel attacks where we looked at protection mechanisms against logical side channel attacks using the routers of a Network on Chip instead of the nodes/IP. This has several benefits.

In this paper, a new countermeasure for AES is presented which is based on confusion

This paper presents a constant time hardware implementation of the lattice algorithm Streamlined NTRU Prime

In this paper we present a new technique that could be used in asymetric cryptography to protect against side channel attacks. The technique is based on exploring multiple bits of the keys simultaneously and comes with 0 overhead. This paper is submitted in september 2020.

Activation sparsity results in performance improvement in sparsity aware neural network accelerators. As the dominant operation in DNN is multiply-accumulator of activation and weights, skipping operations with at least one zero operands can result in efficient deep learning inference. While spatial sparsification of activation is a well-known method, the introduction of spatiotemporal sparsity in DNN is less explored. On the other hand, temporal sparsity exploitation is one of the main features in neuromorphic processing and bio-inspired spiking neural networks. In this paper, we introduce a new layer called delta_activation layer to induce temporal sparsity in any DNN model during training. We reported almost 5x improvement in activation sparsity. As the application domain in DNN is moving from static signal processing (e.g. image processing) toward more streaming signal processing (e.g. video and audio), we believe it makes sense to adapt temporal sparsity techniques in the conventional DNN frameworks. (status: submitted, pending review)

The development of brain-inspired neuromorphic computing architectures as a paradigm for Artificial Intelligence (AI) on the edge is a candidate solution that can meet strict energy and cost reduction constraints in applications areas of smart sensing technologies. Towards this goal, we present μBrain: an event-driven digital architecture that exploits event-based processing to reduce the overall energy consumption of an always-on system. We present an instantiation of the μBrain architecture in 40nm Complementary Metal Oxide Semiconductor (CMOS) digital technology and demonstrate its efficiency in an Internet of Things (IoT) radar signal classification application, with a power consumption of 70μW and energy consumption of 340pJ per classification. We demonstrate that μBrain offers near-sensor processing, it is fully synthesizable, and it allows for a fast development-to-deployment cycle in Application Specific Integrated Circuits (ASIC). This architecture permits low-development costs while taking advantage of digital scaling in advanced node technologies.

The rapidly increasing use of wireless technologies in critical applications as industrial automation is inevitably leading to more interferences and the associated communication problems. Therefore, monitoring solutions for wireless systems have attracted more attention in the recent years. This paper presents a new method to improve monitoring technologies for spectral analysis. Machine Learning-based image processing is used to detect individual packets of different wireless technologies and calculate statistics automatically and in real time. Using IEEE 802.11 as an example, it can be shown that packets and packet collisions can be detected with a high degree of accuracy.

For low frequencies, electromagnetic shielding and scattering of multiple cylindrical shells can be calculated through Gaussian elimina-tion of the Helmholtz equations for the fields and the boundary conditions at each interface. Numerical stability at higher frequencies is improved by constructing the equations differently: By multiplying the matrix found through the boundary equations at each interface.