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Faculty of Technology, Design and Environment
An efficient memristor MIN function based activation circuit is presented for memristive neuromorphic systems, using only two memristors and a comparator. The ReLU activation function is approximated using this circuit. The ReLU activation function helps to significantly reduce the time and computational cost of training in neuromorphic systems due to its simplicity and effectiveness in deep neural networks. A multilayer neural network is simulated using this activation circuit in addition to traditional memristor crossbar arrays. The results illustrate that the proposed circuit is able to perform training effectively with significant savings in time and area in memristor crossbar based neural networks.
This paper presents a novel low-complexity cross parity code, with a wide range of multiple bit error correction capability at a lower overhead, for improving the reliability in circuits over GF(2m). For an m input circuit, the proposed scheme can correct m ≤ Dw≤ 3m/2 -1 multiple error combinations out of all the possible 2m - 1 errors, which is superior to many existing approaches. From the mathematical and practical evaluations, the best case error correction is m/2 bit errors. Tests on 80-bit parallel and, for the first time, on 163-bit Federal Information Processing Standard/National Institute of Standards and Technology (FIPS/NIST) standard word-level Galois field (GF) multipliers, suggest that it requires only 106% and 170% area overheads, respectively, which is lower than the existing approaches, while error injection-based behavioral analysis demonstrates its wider error correction capability.
This study presents a simplified structure of bit parallel systolic multiplier over Galois fields (GFs) over the set GF(2m) suitable for cryptographic hardware implementation. A redundant standard basis representation with the irreducible all one polynomial is considered. The systolic multiplier consists of (m+1)2 identical cells, each consisting of one two-input AND gate, one two-input XOR gate and two one-bit latches. This architecture is well suited to very large-scale integration implementation because of its regularity modular structure and unidirectional data flow. The proposed multipliers have clock cycle latency of (m +1). This architecture has a total reduction of m2 D-flip-flops compared to earlier bit parallel systolic multiplication architecture. As the finite-field multiplier is one of the complex blocks in cryptographic hardware and need secure testability to avoid unwanted access into the on-chip security blocks, the authors also introduce an on-chip testing scheme. The authors propose a test generation technique for detecting stuck-at fault (SAF), transition delay fault (TDF), stuck-open fault (SOF) and path delay faults (PDFs) at the gate and cell level in the systolic architecture. The authors also show that realistic sequential cell fault can be detected only by 12 single input change test vectors in the complete systolic multiplier over GF(2m). The proposed technique derives test vectors from the cell expressions of systolic multipliers without any requirement of an automatic test pattern generation tool. The complete systolic architecture is C-testable for SAF, TDF, SOF and PDF with only 12 constant tests. The test vectors are independent of the multiplier size. The test set provides 100% single SAF, TDF, SOF and PDF coverage.
Motivated by the problems associated with soft errors in digital circuits and fault-related attacks in cryptographic hardware, a systematic method for designing single error correcting multiplier circuits is presented for finite fields or Galois fields over GF(2m). Multiple parity predictions to correct single errors based on the Hamming principles are used. The expressions for the parity prediction are derived from the input operands, and are based on the primitive polynomials of the fields. This technique, when compared with existing ones, gives better performance. It is shown that single error correction (SEC) multipliers over GF(2m) require slightly over 100% extra hardware, whereas with the traditional SEC techniques, this figure is more than 200%. Since single bit internal faults can cause multiple faults in the outputs, this has also been addressed here by using multiple Hamming codes with optimised hardware.
Resistive memory, also known as memristor, is an emerging potential successor to traditional CMOS charge based memories. Memristors have also recently been proposed as a promising candidate for several additional applications such as logic design, sensing, non-volatile storage, neuromorphic computing, Physically Unclonable Functions (PUFs), Contentaddressable memory (CAM) and reconfigurable computing. In this paper, we explore three unique applications of memristor technology based implementations, specifically from the perspective of sensing, logic, in-memory computing and their solutions. We review solar cell health monitoring and diagnosis, describe the proposed solutions, and provide directions in memristive gas sensing and in-memory computing. For the gas sensor application, in order to determine the number of memristors to ensure a certain level of accuracy in sensitivity, a technique to optimize the sensor array based on an acceptable sensitivity variation and minimum sensitivity margin is presented. These "out-of-the-box" emerging ideas for applications of memristive devices in enhancing robustness and, at the same time, how the requirements of robust design are enabling unconventional use of the devices. To this end, the papers considers some examples of this mutual interaction.
Memristors are an attractive option for use in future architectures due to their non-volatility, high density and low power operation. Gas sensing is one of the proposed application of memristive devices. In spite of these advantages, memristors are susceptible to defect densities due to the nondeterministic nature of nano-scale fabrication. In this paper, a novel spice memristor model incorporating fault models that emulates the gas sensing behaviour with/without faults is developed for simulation and integration with design automation tools. Our simulation results show that the proposed non-linear model detects the presence of the oxidising/reducing gas and analyses the defects/faults affecting the functionality of the sensor.
Recent studies have shown that an attacker can retrieve confidential information from cryptographic hardware (e.g. the secret key) by introducing internal faults. A secure and reliable implementation of cryptographic algorithms in hardware must be able to detect or correct such malicious attacks. Error detection/correction (EDC), through fault tolerance, could be an effective way to mitigate such fault attacks in cryptographic hardware. To this end, we analyze the area, delay, and power overhead for designing the S-Box, which is one of the main complex blocks in the Advanced Encryption Standard (AES), with error detection and correction capability. We use multiple Parity Predictions (PPs), based on various error correcting codes, to detect and correct errors. Various coding techniques are presented, which include simple parity prediction, split parity codes, Hamming, Hsiao, and LDPC codes. The S-Box, GF(p), and PP circuits are synthesized from the specifications, while the decoding and correction circuits are combined to form the complete designs. The analysis shows a comparison of the different approaches characterized by their error detection capability.