Research: AI Hardware

Past Projects Patents Publications Funding Agencies

AI Hardware

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Our research focuses on developing specialized systems that efficiently support the growing demands of artificial intelligence workloads. As AI algorithms become more complex and data-intensive, we recognize the challenges that traditional hardware architectures face in terms of performance, power consumption, and scalability. To address these limitations, we explore innovative hardware approaches and technologies that make AI processing faster, more efficient, and secure. Our work spans across Compute-In-Memory and Compute-Near-Memory architectures, neuromimetic devices, co-design, AI-driven secure hardware, and AI for hardware design, each contributing to the next generation of AI systems.

B. Neuro-mimetic devices

In this area, our research is centered on developing devices that mimic the functionality of biological neurons and synapses. By emulating synaptic plasticity and neural dynamics, these devices enable us to build ultra-low-power, adaptive computing systems that can learn and process information in a way that is similar to the human brain.

Publications:
  1. Yu, Eunseon, Gaurav K, Utkarsh Saxena, and Kaushik Roy. "Ferroelectric capacitors and field-effect transistors as in-memory computing elements for machine learning workloads." Scientific Reports, 14(1), p.9426.

    Abstract: This study discusses the feasibility of Ferroelectric Capacitors (FeCaps) and Ferroelectric Field-Effect Transistors (FeFETs) as In-Memory Computing (IMC) elements to accelerate machine learning (ML) workloads. We conducted an exploration of device fabrication and proposed system-algorithm co-design to boost performance. A novel FeCap device, incorporating an interfacial layer (IL) and (HZO), ensures a reduction in operating voltage and enhances HZO scaling while being compatible with CMOS circuits. The IL also enriches ferroelectricity and retention properties. When integrated into crossbar arrays, FeCaps and FeFETs demonstrate their effectiveness as IMC components, eliminating sneak paths and enabling selector-less operation, leading to notable improvements in energy efficiency and area utilization. However, it is worth noting that limited capacitance ratios in FeCaps introduced errors in multiply-and-accumulate (MAC) computations. The proposed co-design approach helps in mitigating these errors and achieves high accuracy in classifying the CIFAR-10 dataset, elevating it from a baseline of 10% to 81.7%. FeFETs in crossbars, with a higher on-off ratio, outperform FeCaps, and our proposed charge-based sensing scheme achieved at least an order of magnitude reduction in power consumption, compared to prevalent current-based methods.

  2. Wang, Cheng, Chankyu Lee, and Kaushik Roy. "Noise resilient leaky integrate-and-fire neurons based on multi-domain spintronic devices." Scientific Reports, 12(1), p.8361.

    Abstract: The capability of emulating neural functionalities efficiently in hardware is crucial for building neuromorphic computing systems. While various types of neuro-mimetic devices have been investigated, it remains challenging to provide a compact device that can emulate spiking neurons. In this work, we propose a non-volatile spin-based device for efficiently emulating a leaky integrate-and-fire neuron. By incorporating an exchange-coupled composite free layer in spin-orbit torque magnetic tunnel junctions, multi-domain magnetization switching dynamics is exploited to realize gradual accumulation of membrane potential for a leaky integrate-and-fire neuron with compact footprints. The proposed device offers significantly improved scalability compared with previously proposed spin-based neuro-mimetic implementations while exhibiting high energy efficiency and good controllability. Moreover, the proposed neuron device exhibits a varying leak constant and a varying membrane resistance that are both dependent on the magnitude of the membrane potential. Interestingly, we demonstrate that such device-inspired dynamic behaviors can be incorporated to construct more robust spiking neural network models, and find improved resiliency against various types of noise injection scenarios. The proposed spintronic neuro-mimetic devices may potentially open up exciting opportunities for the development of efficient and robust neuro-inspired computational hardware.

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