IMSE-CNM

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.

This paper presents a new strategy and circuit configuration composed of serially-connected PMOS devices operating in the subthreshold region for implementing ultra-highvalue resistors required in very low-frequency active-RC filters and bio-amplifiers. Depending on the application, signal bandwidth for instance in bio-amplifiers may vary from a few mHz up to a maximum of 10 kHz. Three different resistor structures are proposed to achieve ultra-high resistance. While ranging in the order of several TY, the proposed ultra-high-resistance pseudoresistors occupy a small on-chip silicon area, which is one of the main issues in the design of analog front-end circuits in ultra-low power implantable biomedical microsystems. In addition, these ultra-high-value resistors lead to the use of a small capacitance to create a very small cut-off frequency. Therefore, the large area to implement capacitances is also considerably reduced. The proposed resistor structures have very small variations about 7% and 12% in a wide input voltage range (-0.5 V +0.5 V), thus significantly improving the total harmonic distortion of bioamplifiers and the analog front-end of the system. Simulation results of different circuits designed in a 180nm CMOS technology, are shown to demonstrate the advantages of the proposed ultra-high-resistance pseudo-resistors.