arXiv subjects

Anupam Chattopadhyay

Publications and source records attributed to Anupam Chattopadhyay.

At least 55 records · Page 3Linked to original sources

arXiv2023

Integrated Architecture for Neural Networks and Security Primitives using RRAM Crossbar

This paper proposes an architecture that integrates neural networks (NNs) and hardware security modules using a single resistive random access memory (RRAM) crossbar. The proposed architecture enables using a single crossbar to implement NN, true random number generator (TRNG), and physical unclonable function (PUF) applications while exploiting the multi-state storage characteristic of the RRAM crossbar for the vector-matrix multiplication operation required for the implementation of NN. The TRNG is implemented by utilizing the crossbar's variation in device switching thresholds to generate random bits. The PUF is implemented using the same crossbar initialized as an entropy source for the TRNG. Additionally, the weights locking concept is introduced to enhance the security of NNs by preventing unauthorized access to the NN weights. The proposed architecture provides flexibility to configure the RRAM device in multiple modes to suit different applications. It shows promise in achieving a more efficient and compact design for the hardware implementation of NNs and security primitives.

cs.ET↗

arXiv2023

A Higher Radix Architecture for Quantum Carry-lookahead Adder

In this paper, we propose an efficient quantum carry-lookahead adder based on the higher radix structure. For the addition of two $n$-bit numbers, our adder uses $O(n)-O(\frac{n}{r})$ qubits and $O(n)+O(\frac{n}{r})$ T gates to get the correct answer in T-depth $O(r)+O(\log{\frac{n}{r}})$, where $r$ is the radix. Quantum carry-lookahead adder has already attracted some attention because of its low T-depth. Our work further reduces the overall cost by introducing a higher radix layer. By analyzing the performance in T-depth, T-count, and qubit count, it is shown that the proposed adder is superior to existing quantum carry-lookahead adders. Even compared to the Draper out-of-place adder which is very compact and efficient, our adder is still better in terms of T-count.

quant-ph↗

arXiv2022

Robust Quantum Arithmetic Operations with Intermediate Qutrits in the NISQ-era

Numerous scientific developments in this NISQ-era (Noisy Intermediate Scale Quantum) have raised the importance for quantum algorithms relative to their conventional counterparts due to its asymptotic advantage. For resource estimates in several quantum algorithms, arithmetic operations are crucial. With resources reported as a number of Toffoli gates or T gates with/without ancilla, several efficient implementations of arithmetic operations, such as addition/subtraction, multiplication/division, square root, etc., have been accomplished in binary quantum systems. More recently, it has been shown that intermediate qutrits may be employed in the ancilla-free frontier zone, enabling us to function effectively there. In order to achieve efficient implementation of all the above-mentioned quantum arithmetic operations with regard to gate count and circuit-depth without T gate and ancilla, we have included an intermediate qutrit method in this paper. Future research aiming at reducing costs while taking into account arithmetic operations for computing tasks might be guided by our resource estimations using intermediate qutrits. Therefore, the enhancements are examined in relation to the fundamental arithmetic circuits. The intermediate qutrit approach necessitates access to higher energy levels, making the design susceptible to errors. We nevertheless demonstrate that the percentage decrease in the probability of error is significant due to the fact that we achieve circuit efficiency by reducing circuit-depth in comparison to qubit-only works.

quant-ph↗

arXiv2022

QuDiet: A Classical Simulation Platform for Qubit-Qudit Hybrid Quantum Systems

In the recent years, numerous research advancements have extended the limit of classical simulation of quantum algorithms. Although, most of the state-of-the-art classical simulators are only limited to binary quantum systems, which restrict the classical simulation of higher-dimensional quantum computing systems. Through recent developments in higher-dimensional quantum computing systems, it is realized that implementing qudits improves the overall performance of a quantum algorithm by increasing memory space and reducing the asymptotic complexity of a quantum circuit. Hence, in this article, we introduce \textbf{QuDiet}, a state-of-the-art user-friendly python-based higher-dimensional quantum computing simulator. \textbf{QuDiet} offers multi-valued logic operations by utilizing generalized quantum gates with an abstraction so that any naive user can simulate qudit systems with ease as compared to the existing ones. We simulate various benchmark quantum circuits in \textbf{QuDiet} and show the considerable speedup in simulation time as compared to the other simulators without loss in precision. Finally, \textbf{QuDiet} provides a full qubit-qudit hybrid quantum simulator package with quantum circuit templates of well-known quantum algorithms for fast prototyping and simulation. The complete code and packages of \textbf{QuDiet} is available at https://github.com/LegacYFTw/QuDiet so that other platforms can incorporate it as a classical simulation option for qubit-qudit hybrid systems to their platforms.

quant-ph↗

arXiv2022

Hardware Security Primitives using Passive RRAM Crossbar Array: Novel TRNG and PUF Designs

With rapid advancements in electronic gadgets, the security and privacy aspects of these devices are significant. For the design of secure systems, physical unclonable function (PUF) and true random number generator (TRNG) are critical hardware security primitives for security applications. This paper proposes novel implementations of PUF and TRNGs on the RRAM crossbar structure. Firstly, two techniques to implement the TRNG in the RRAM crossbar are presented based on write-back and 50% switching probability pulse. The randomness of the proposed TRNGs is evaluated using the NIST test suite. Next, an architecture to implement the PUF in the RRAM crossbar is presented. The initial entropy source for the PUF is used from TRNGs, and challenge-response pairs (CRPs) are collected. The proposed PUF exploits the device variations and sneak-path current to produce unique CRPs. We demonstrate, through extensive experiments, reliability of 100%, uniqueness of 47.78%, uniformity of 49.79%, and bit-aliasing of 48.57% without any post-processing techniques. Finally, the design is compared with the literature to evaluate its implementation efficiency, which is clearly found to be superior to the state-of-the-art.

cs.CR↗

arXiv2022

PA-PUF: A Novel Priority Arbiter PUF

This paper proposes a 3-input arbiter-based novel physically unclonable function (PUF) design. Firstly, a 3-input priority arbiter is designed using a simple arbiter, two multiplexers (2:1), and an XOR logic gate. The priority arbiter has an equal probability of 0's and 1's at the output, which results in excellent uniformity (49.45%) while retrieving the PUF response. Secondly, a new PUF design based on priority arbiter PUF (PA-PUF) is presented. The PA-PUF design is evaluated for uniqueness, non-linearity, and uniformity against the standard tests. The proposed PA-PUF design is configurable in challenge-response pairs through an arbitrary number of feed-forward priority arbiters introduced to the design. We demonstrate, through extensive experiments, reliability of 100% after performing the error correction techniques and uniqueness of 49.63%. Finally, the design is compared with the literature to evaluate its implementation efficiency, where it is clearly found to be superior compared to the state-of-the-art.

cs.CR↗

arXiv2022

Optimal Codeword Construction for DNA-based Finite Automata

Biomolecular computation has emerged as an important area of computer science research due to its high information density, immense parallelism opportunity along with potential applications in cryptography, genetic engineering and bioinformatics. Computational frameworks using DNA molecules have been proposed in the literature to accomplish varied tasks such as simulating logical operations, performing matrix multiplication, and encoding instances of NP-hard problems. In one of the key applications, several studies have proposed construction of finite automata using DNA hybridisation and ligation. The state and symbol encoding of these finite automata are done manually. In this manuscript, we study the codeword construction problem for this approach. We derive exact theoretical bounds on the number of symbols and states in the finite automata and also obtain the complete set of symbols in a specific case. For automatic encoding, two different solutions, based on a heuristic and on Integer Linear Programming (ILP), are proposed. Furthermore, we propose an early simulation-based validation of laboratory experiments. Our proposed flow accepts a finite automaton, automatically encodes the symbols for the actual experiments and executes the simulation step-by-step.

cs.FL↗

arXiv2022

Intermediate Qutrit-based Improved Quantum Arithmetic Operations with Application on Financial Derivative Pricing

In some quantum algorithms, arithmetic operations are of utmost importance for resource estimation. In binary quantum systems, some efficient implementation of arithmetic operations like, addition/subtraction, multiplication/division, square root, exponential and arcsine etc. have been realized, where resources are reported as a number of Toffoli gates or T gates with ancilla. Recently it has been demonstrated that intermediate qutrits can be used in place of ancilla, allowing us to operate efficiently in the ancilla-free frontier zone. In this article, we have incorporated intermediate qutrit approach to realize efficient implementation of all the quantum arithmetic operations mentioned above with respect to gate count and circuit-depth without T gate and ancilla. Our resource estimates with intermediate qutrits could guide future research aimed at lowering costs considering arithmetic operations for computational problems. As an application of computational problems, related to finance, are poised to reap the benefit of quantum computers, in which quantum arithmetic circuits are going to play an important role. In particular, quantum arithmetic circuits of arcsine and square root are necessary for path loading using the re-parameterization method, as well as the payoff calculation for derivative pricing. Hence, the improvements are studied in the context of the core arithmetic circuits as well as the complete application of derivative pricing. Since our intermediate qutrit approach requires to access higher energy levels, making the design prone to errors, nevertheless, we show that the percentage decrease in the probability of error is significant owing to the fact that we achieve circuit robustness compared to qubit-only works.

quant-ph↗

arXiv2021

The Bitlet Model: A Parameterized Analytical Model to Compare PIM and CPU Systems

Nowadays, data-intensive applications are gaining popularity and, together with this trend, processing-in-memory (PIM)-based systems are being given more attention and have become more relevant. This paper describes an analytical modeling tool called Bitlet that can be used, in a parameterized fashion, to estimate the performance and the power/energy of a PIM-based system and thereby assess the affinity of workloads for PIM as opposed to traditional computing. The tool uncovers interesting tradeoffs between, mainly, the PIM computation complexity (cycles required to perform a computation through PIM), the amount of memory used for PIM, the system memory bandwidth, and the data transfer size. Despite its simplicity, the model reveals new insights when applied to real-life examples. The model is demonstrated for several synthetic examples and then applied to explore the influence of different parameters on two systems - IMAGING and FloatPIM. Based on the demonstrations, insights about PIM and its combination with CPU are concluded.

cs.AR↗

arXiv2020

Spatially Correlated Patterns in Adversarial Images

Adversarial attacks have proved to be the major impediment in the progress on research towards reliable machine learning solutions. Carefully crafted perturbations, imperceptible to human vision, can be added to images to force misclassification by an otherwise high performing neural network. To have a better understanding of the key contributors of such structured attacks, we searched for and studied spatially co-located patterns in the distribution of pixels in the input space. In this paper, we propose a framework for segregating and isolating regions within an input image which are particularly critical towards either classification (during inference), or adversarial vulnerability or both. We assert that during inference, the trained model looks at a specific region in the image, which we call Region of Importance (RoI); and the attacker looks at a region to alter/modify, which we call Region of Attack (RoA). The success of this approach could also be used to design a post-hoc adversarial defence method, as illustrated by our observations. This uses the notion of blocking out (we call neutralizing) that region of the image which is highly vulnerable to adversarial attacks but is not important for the task of classification. We establish the theoretical setup for formalising the process of segregation, isolation and neutralization and substantiate it through empirical analysis on standard benchmarking datasets. The findings strongly indicate that mapping features into the input space preserves the significant patterns typically observed in the feature-space while adding major interpretability and therefore simplifies potential defensive mechanisms.

cs.AI↗

arXiv2020

FlexWatts: A Power- and Workload-Aware Hybrid Power Delivery Network for Energy-Efficient Microprocessors

Modern client processors typically use one of three commonly-used power delivery network (PDN): 1) motherboard voltage regulators (MBVR), 2) integrated voltage regulators (IVR), and 3) low dropout voltage regulators (LDO). We observe that the energy-efficiency of each of these PDNs varies with the processor power (e.g., thermal design power (TDP) and dynamic power-state) and workload characteristics. This leads to energy inefficiency and performance loss, as modern client processors operate across a wide spectrum of power consumption and execute a wide variety of workloads. We propose FlexWatts, a hybrid adaptive PDN for modern client processors whose goal is to provide high energy-efficiency across the processor's wide range of power consumption and workloads by dynamically allocating PDNs to processor domains. FlexWatts is based on three key ideas. First, it combines IVRs and LDOs in a novel way to share multiple on-chip and off-chip resources. This hybrid PDN is allocated for processor domains with a wide power consumption range and it dynamically switches between two modes: IVR-Mode and LDO-Mode, depending on the power consumption. Second, for all other processor domains, FlexWatts statically allocates off-chip VRs. Third, FlexWatts introduces a prediction algorithm that switches the hybrid PDN to the mode that is the most beneficial. To evaluate the tradeoffs of PDNs, we develop and open-source PDNspot, the first validated architectural PDN model that enables quantitative analysis of PDN metrics. Using PDNspot, we evaluate FlexWatts on a wide variety of SPEC CPU2006, 3DMark06, and battery life workloads against IVR, the state-of-the-art PDN in modern client processors. For a 4W TDP processor, FlexWatts improves the average performance of the SPEC CPU2006 and 3DMark06 workloads by 22% and 25%, respectively. FlexWatts has comparable cost and area overhead to IVR.

cs.AR↗

arXiv2020

CONTRA: Area-Constrained Technology Mapping Framework For Memristive Memory Processing Unit

Data-intensive applications are poised to benefit directly from processing-in-memory platforms, such as memristive Memory Processing Units, which allow leveraging data locality and performing stateful logic operations. Developing design automation flows for such platforms is a challenging and highly relevant research problem. In this work, we investigate the problem of minimizing delay under arbitrary area constraint for MAGIC-based in-memory computing platforms. We propose an end-to-end area constrained technology mapping framework, CONTRA. CONTRA uses Look-Up Table(LUT) based mapping of the input function on the crossbar array to maximize parallel operations and uses a novel search technique to move data optimally inside the array. CONTRA supports benchmarks in a variety of formats, along with crossbar dimensions as input to generate MAGIC instructions. CONTRA scales for large benchmarks, as demonstrated by our experiments. CONTRA allows mapping benchmarks to smaller crossbar dimensions than achieved by any other technique before, while allowing a wide variety of area-delay trade-offs. CONTRA improves the composite metric of area-delay product by 2.1x to 13.1x compared to seven existing technology mapping approaches.

cs.AR↗

arXiv2020

RAPPER: Ransomware Prevention via Performance Counters

Ransomware can produce direct and controllable economic loss, which makes it one of the most prominent threats in cyber security. As per the latest statistics, more than half of malwares reported in Q1 of 2017 are ransomwares and there is a potent threat of a novice cybercriminals accessing ransomware-as-a-service. The concept of public-key based data kidnapping and subsequent extortion was introduced in 1996. Since then, variants of ransomware emerged with different cryptosystems and larger key sizes, the underlying techniques remained same. Though there are works in literature which proposes a generic framework to detect the crypto ransomwares, we present a two step unsupervised detection tool which when suspects a process activity to be malicious, issues an alarm for further analysis to be carried in the second step and detects it with minimal traces. The two step detection framework- RAPPER uses Artificial Neural Network and Fast Fourier Transformation to develop a highly accurate, fast and reliable solution to ransomware detection using minimal trace points. We also introduce a special detection module for successful identification of disk encryption processes from potential ransomware operations, both having similar characteristics but with different objective. We provide a comprehensive solution to tackle almost all scenarios (standard benchmark, disk encryption and regular high computational processes) pertaining to the crypto ransomwares in light of software security.

cs.CR↗

arXiv2020

Towards Secure Composition of Integrated Circuits and Electronic Systems: On the Role of EDA

Modern electronic systems become evermore complex, yet remain modular, with integrated circuits (ICs) acting as versatile hardware components at their heart. Electronic design automation (EDA) for ICs has focused traditionally on power, performance, and area. However, given the rise of hardware-centric security threats, we believe that EDA must also adopt related notions like secure by design and secure composition of hardware. Despite various promising studies, we argue that some aspects still require more efforts, for example: effective means for compilation of assumptions and constraints for security schemes, all the way from the system level down to the "bare metal"; modeling, evaluation, and consideration of security-relevant metrics; or automated and holistic synthesis of various countermeasures, without inducing negative cross-effects. In this paper, we first introduce hardware security for the EDA community. Next we review prior (academic) art for EDA-driven security evaluation and implementation of countermeasures. We then discuss strategies and challenges for advancing research and development toward secure composition of circuits and systems.

cs.CR↗

arXiv2019

MUQUT: Multi-Constraint Quantum Circuit Mapping on Noisy Intermediate-Scale Quantum Computers

Rapid advancement in the domain of quantum technologies has opened up researchers to the real possibility of experimenting with quantum circuits and simulating small-scale quantum programs. Nevertheless, the quality of currently available qubits and environmental noise poses a challenge in the smooth execution of the quantum circuits. Therefore, efficient design automation flows for mapping a given algorithm to the Noisy Intermediate Scale Quantum (NISQ) computer becomes of utmost importance. State-of-the-art quantum design automation tools are primarily focused on reducing logical depth, gate count and qubit count with the recent emphasis on topology-aware (nearest-neighbor compliance) mapping. In this work, we extend the technology mapping flows to simultaneously consider the topology and gate fidelity constraints while keeping logical depth and gate count as optimization objectives. We provide a comprehensive problem formulation and multi-tier approach towards solving it. The proposed automation flow is compatible with commercial quantum computers, such as IBM QX and Rigetti. Our simulation results over 10 quantum circuit benchmarks show that the fidelity of the circuit can be improved up to 3.37X with an average improvement of 1.87X.

quant-ph↗

arXiv2019

The Bitlet Model: Defining a Litmus Test for the Bitwise Processing-in-Memory Paradigm

This paper describes an analytical modeling tool called Bitlet that can be used, in a parameterized fashion, to understand the affinity of workloads to processing-in-memory (PIM) as opposed to traditional computing. The tool uncovers interesting trade-offs between operation complexity (cycles required to perform an operation through PIM) and other key parameters, such as system memory bandwidth, data transfer size, the extent of data alignment, and effective memory capacity involved in PIM computations. Despite its simplicity, the model has already proven useful. In the future, we intend to extend and refine Bitlet to further increase its utility.

cs.AR↗

arXiv2019

New techniques for fault-tolerant decomposition of Multi-Controlled Toffoli gate

Physical implementation of scalable quantum architectures faces an immense challenge in form of fragile quantum states. To overcome it, quantum architectures with fault tolerance is desirable. This is achieved currently by using surface code along with a transversal gate set. This dictates the need for decomposition of universal Multi Control Toffoli~(MCT) gates using a transversal gate set. Additionally, the transversal non-Clifford phase gate incurs high latency which makes it an important factor to consider during decomposition.Besides, the decomposition of large Multi-control Toffoli~(MCT) gate without ancilla presents an additional hurdle. In this manuscript, we address both of these issues by introducing Clifford+$Z_N$ gate library. We present an ancilla free decomposition of MCT gates with linear phase depth and quadratic phase count. Furthermore, we provide a technique for decomposition of MCT gates in unit phase depth using the Clifford+$Z_N$ library, albeit at the cost of ancillary lines and quadratic phase count.

cs.ET↗

arXiv2018

Criticality Aware Soft Error Mitigation in the Configuration Memory of SRAM based FPGA

Efficient low complexity error correcting code(ECC) is considered as an effective technique for mitigation of multi-bit upset (MBU) in the configuration memory(CM)of static random access memory (SRAM) based Field Programmable Gate Array (FPGA) devices. Traditional multi-bit ECCs have large overhead and complex decoding circuit to correct adjacent multibit error. In this work, we propose a simple multi-bit ECC which uses Secure Hash Algorithm for error detection and parity based two dimensional Erasure Product Code for error correction. Present error mitigation techniques perform error correction in the CM without considering the criticality or the execution period of the tasks allocated in different portion of CM. In most of the cases, error correction is not done in the right instant, which sometimes either suspends normal system operation or wastes hardware resources for less critical tasks. In this paper,we advocate for a dynamic priority-based hardware scheduling algorithm which chooses the tasks for error correction based on their area, execution period and criticality. The proposed method has been validated in terms of overhead due to redundant bits, error correction time and system reliability

cs.AR↗