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Md Mazharul Islam

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Reimagining Sense Amplifiers: Harnessing Phase Transition Materials for Current and Voltage Sensing

Aug 30, 2023
Md Mazharul Islam, Shamiul Alam, Mohammad Adnan Jahangir, Garrett S. Rose, Suman Datta, Vijaykrishnan Narayanan, Sumeet Kumar Gupta, Ahmedullah Aziz

Energy-efficient sense amplifier (SA) circuits are essential for reliable detection of stored memory states in emerging memory systems. In this work, we present four novel sense amplifier (SA) topologies based on phase transition material (PTM) tailored for non-volatile memory applications. We utilize the abrupt switching and volatile hysteretic characteristics of PTMs which enables efficient and fast sensing operation in our proposed SA topologies. We provide comprehensive details of their functionality and assess how process variations impact their performance metrics. Our proposed sense amplifier topologies manifest notable performance enhancement. We achieve a ~67% reduction in sensing delay and a ~80% decrease in sensing power for current sensing. For voltage sensing, we achieve a ~75% reduction in sensing delay and a ~33% decrease in sensing power. Moreover, the proposed SA topologies exhibit improved variation robustness compared to conventional SAs. We also scrutinize the dependence of transistor mirroring window and PTM transition voltages on several device parameters to determine the optimum operating conditions and stance of tunability for each of the proposed SA topologies.

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A Deep Dive into the Design Space of a Dynamically Reconfigurable Cryogenic Spiking Neuron

Aug 30, 2023
Md Mazharul Islam, Shamiul Alam, Catherine D Schuman, Md Shafayat Hossain, Ahmedullah Aziz

Spiking neural network offers the most bio-realistic approach to mimic the parallelism and compactness of the human brain. A spiking neuron is the central component of an SNN which generates information-encoded spikes. We present a comprehensive design space analysis of the superconducting memristor (SM)-based electrically reconfigurable cryogenic neuron. A superconducting nanowire (SNW) connected in parallel with an SM function as a dual-frequency oscillator and two of these oscillators can be coupled to design a dynamically tunable spiking neuron. The same neuron topology was previously proposed where a fixed resistance was used in parallel with the SNW. Replacing the fixed resistance with the SM provides an additional tuning knob with four distinct combinations of SM resistances, which improves the reconfigurability by up to ~70%. Utilizing an external bias current (Ibias), the spike frequency can be modulated up to ~3.5 times. Two distinct spike amplitudes (~1V and ~1.8 V) are also achieved. Here, we perform a systematic sensitivity analysis and show that the reconfigurability can be further tuned by choosing a higher input current strength. By performing a 500-point Monte Carlo variation analysis, we find that the spike amplitude is more variation robust than spike frequency and the variation robustness can be further improved by choosing a higher Ibias. Our study provides valuable insights for further exploration of materials and circuit level modification of the neuron that will be useful for system-level incorporation of the neuron circuit

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Cryogenic Neuromorphic Hardware

Mar 25, 2022
Md Mazharul Islam, Shamiul Alam, Md Shafayat Hossain, Kaushik Roy, Ahmedullah Aziz

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The revolution in artificial intelligence (AI) brings up an enormous storage and data processing requirement. Large power consumption and hardware overhead have become the main challenges for building next-generation AI hardware. Therefore, it is imperative to look for a new architecture capable of circumventing these bottlenecks of conventional von Neumann architecture. Since the human brain is the most compact and energy-efficient intelligent device known, it was intuitive to attempt to build an architecture that could mimic our brain, and so the chase for neuromorphic computing began. While relentless research has been underway for years to minimize the power consumption in neuromorphic hardware, we are still a long way off from reaching the energy efficiency of the human brain. Besides, design complexity, process variation, etc. hinder the large-scale implementation of current neuromorphic platforms. Recently, the concept of implementing neuromorphic computing systems in cryogenic temperature has garnered immense attention. Several cryogenic devices can be engineered to work as neuromorphic primitives with ultra-low demand for power. Cryogenic electronics has therefore become a promising exploratory platform for an energy-efficient and bio-realistic neuromorphic system. Here we provide a comprehensive overview of the reported cryogenic neuromorphic hardware. We carefully classify the existing cryogenic neuromorphic hardware into different categories and draw a comparative analysis based on several performance metrics. Finally, we explore the future research prospects to circumvent the challenges associated with the current technologies.

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The Panacea Threat Intelligence and Active Defense Platform

Apr 20, 2020
Adam Dalton, Ehsan Aghaei, Ehab Al-Shaer, Archna Bhatia, Esteban Castillo, Zhuo Cheng, Sreekar Dhaduvai, Qi Duan, Md Mazharul Islam, Younes Karimi, Amir Masoumzadeh, Brodie Mather, Sashank Santhanam, Samira Shaikh, Tomek Strzalkowski, Bonnie J. Dorr

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We describe Panacea, a system that supports natural language processing (NLP) components for active defenses against social engineering attacks. We deploy a pipeline of human language technology, including Ask and Framing Detection, Named Entity Recognition, Dialogue Engineering, and Stylometry. Panacea processes modern message formats through a plug-in architecture to accommodate innovative approaches for message analysis, knowledge representation and dialogue generation. The novelty of the Panacea system is that uses NLP for cyber defense and engages the attacker using bots to elicit evidence to attribute to the attacker and to waste the attacker's time and resources.

* Accepted at STOC 
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