Biblio

Efficient low-power accelerators for Convolutional Neural Networks (CNNs) largely benefit from quantization and approximation, which are typically applied layer-wise for efficient hardware implementation. In this work, we present a novel strategy for efficient combination of these concepts at a deeper level, which is at each channel or kernel. We first apply layer-wise, low bit-width, linear quantization and truncation-based approximate multipliers to the CNN computation. Then, based on a state-of-the-art resiliency analysis, we are able to apply a kernel-wise approximation and quantization scheme with negligible accuracy losses, without further retraining. Our proposed strategy is implemented in a specialized framework for fast design space exploration. This optimization leads to a boost in estimated power savings of up to 34% in residual CNN architectures for image classification, compared to the base quantized architecture.

Modern embedded systems need to cater for several needs depending upon the application domain in which they are deployed. For example, mixed-critically needs to be considered for real-time and safety-critical systems and energy for battery-operated systems. At the same time, many of these systems demand for their reliability and security as well. With electronic systems being used for increasingly varying type of applications, novel challenges have emerged. For example, with the use of embedded systems in increasingly complex applications that execute tasks with varying priorities, mixed-criticality systems present unique challenges to designing reliable systems. The large design space involved in implementing cross-layer reliability in heterogeneous systems, particularly for mixed-critical systems, poses new research problems. Further, malicious security attacks on these systems pose additional extraordinary challenges in the system design. In this paper, we cover both the industry and academia perspectives of the challenges posed by these emergent aspects of system design towards designing highperformance, energy-efficient, reliable and/or secure embedded systems. We also provide our views on paths forward.

Structured Query Language (SQL) is extensively used for storing, manipulating and retrieving information in the relational database management system. Using SQL statements, attackers will try to gain unauthorized access to databases and launch attacks to modify/retrieve the stored data, such attacks are called as SQL injection attacks. Such SQL Injection (SQLi) attacks tops the list of web application security risks of all the times. Identifying and mitigating the potential SQL attack statements before their execution can prevent SQLi attacks. Various techniques are proposed in the literature to mitigate SQLi attacks. In this paper, a random decision forest approach is introduced to mitigate SQLi attacks. From the experimental results, we can infer that the proposed approach achieves a precision of 97% and an accuracy of 95%.

Existing logic-locking attacks are known to successfully decrypt functionally correct key of a locked combinational circuit. It is possible to extend these attacks to real-world Silicon-based Intellectual Properties (IPs, which are sequential circuits) through scan-chains by selectively initializing the combinational logic and analyzing the responses. In this paper, we propose SeqL, which achieves functional isolation and locks selective flip-flop functional-input/scan-output pairs, thus rendering the decrypted key functionally incorrect. We conduct a formal study of the scan-locking problem and demonstrate automating our proposed defense on any given IP. We show that SeqL hides functionally correct keys from the attacker, thereby increasing the likelihood of the decrypted key being functionally incorrect. When tested on pipelined combinational benchmarks (ISCAS, MCNC), sequential benchmarks (ITC) and a fully-fledged RISC-V CPU, SeqL gave 100% resilience to a broad range of state-of-the-art attacks including SAT [1], Double-DIP [2], HackTest [3], SMT [4], FALL [5], Shift-and-Leak [6] and Multi-cycle attacks [7].