IMSE-CNM

The advent of quantum computing with high processing capabilities will enable brute force attacks in short periods of time, threatening current secure communication channels. To mitigate this situation, post-quantum cryptography (PQC) algorithms have emerged. Among the algorithms evaluated by NIST in the third round of its PQC contest was the NTRU cryptosystem. The main drawback of this algorithm is the enormous amount of time required for the multiplication of polynomials in both the encryption and decryption processes. Therefore, the strategy of speeding up this algorithm using hardware/software co-design techniques where this operation is executed on specific hardware arises. Using these techniques, this work focuses on the acceleration of polynomial multiplication in the encryption process for resource-constrained devices. For this purpose, several hardware multiplications are analyzed following different strategies, taking into account the fact that there are no possible timing information leaks and that the available resources are optimized as much as possible. The designed multiplier is encapsulated as a fully reusable and parametrizable IP module with standard AXI4-Stream interconnection buses, which makes it easy to integrate into embedded systems implemented on programmable devices from different manufacturers. Depending on the resource constraints imposed, accelerations of up to 30-45 times with respect to the software-level multiplication runtime can be achieved using dedicated hardware, with a device occupancy of around 5%.

The proliferation of devices for the Internet of Things (IoT) and their implication in many activities of our lives have led to a considerable increase in concern about the security of these devices, posing a double challenge for designers and developers of products. On the one hand, the design of new security primitives, suitable for resource-limited devices, can facilitate the inclusion of mechanisms and protocols to ensure the integrity and privacy of the data exchanged over the Internet. On the other hand, the development of techniques and tools to evaluate the quality of the proposed solutions as a step prior to their deployment, as well as to monitor their behavior once in operation against possible changes in operating conditions arising naturally or as a consequence of a stress situation forced by an attacker. To address these challenges, this paper first describes the design of a security primitive that plays an important role as a component of a hardware-based root of trust, as it can act as a source of entropy for True Random Number Generation (TRNG) or as a Physical Unclonable Function (PUF) to facilitate the generation of identifiers linked to the device on which it is implemented. The work also illustrates different software components that allow carrying out a self-assessment strategy to characterize and validate the performance of this primitive in its dual functionality, as well as to monitor possible changes in security levels that may occur during operation as a result of device aging and variations in power supply or operating temperature. The designed PUF/TRNG is provided as a configurable IP module, which takes advantage of the internal architecture of the Xilinx Series-7 and Zynq-7000 programmable devices and incorporates an AXI4-based standard interface to facilitate its interaction with soft- and hard-core processing systems. Several test systems that contain different instances of the IP have been implemented and subjected to an exhaustive set of on-line tests to obtain the metrics that determine its quality in terms of uniqueness, reliability, and entropy characteristics. The results obtained prove that the proposed module is a suitable candidate for various security applications. As an example, an implementation that uses less than 5% of the resources of a low-cost programmable device is capable of obfuscating and recovering 512-bit cryptographic keys with virtually zero error rate.

Extracting information of interest from continuous video streams is a strongly demanded computer vision task. For the realization of this task at the edge using the current de-facto standard approach, i.e., deep learning, it is critical to optimize key performance metrics such as throughput and energy consumption according to prescribed application requirements. This allows achieving timely decision-making while extending the battery lifetime as much as possible. In this context, we propose a method to boost neural-network performance based on a co-execution strategy that exploits hardware heterogeneity on edge platforms. The enabling tool is Apache TVM, a highly efficient machine-learning compiler compatible with a diversity of hardware back-ends. The proposed approach solves the problem of network partitioning and distributes the workloads to make concurrent use of all the processors available on the board following a pipeline scheme. We conducted experiments on various popular CNNs compiled with TVM on the Jetson TX2 platform. The experimental results based on measurements show a significant improvement in throughput with respect to a single-processor execution, ranging from 14% to 150% over all tested networks. Power-efficient configurations were also identified, accomplishing energy reductions above 10%.

This paper reviews the state of the art on bandpass sigma-delta modulators (BP-sigma-deltaMs) intended to digitize radio frequency (RF) signals. A priori, this is the most direct way to implement software-defined radio (SDR) systems since the analog/digital interface is placed closer to the antenna, thus reducing the analog circuitry and doing most of the signal processing in the digital domain. In spite of their higher programmability and scalability, RF BP-sigma-deltaM analog-to-digital converters (ADCs) require more energy to operate in the GHz range as compared with their low-pass (LP) counterparts. This makes conventional direct conversion receivers (DCRs) the commonplace approach due to their overall smaller energy consumption. This paper surveys some circuits and systems techniques which can make RF ADCs and SDR-based transceivers more efficient and feasible to be embedded in mobile terminals.