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Spin Transfer Torque Random Access Memory (STT-RAM) has garnered interest due to its various characteristics such as non-volatility, low leakage power, high density. Its magnetic properties have a vital role in STT switching operations…

In this paper, we develop an in-memory analog computing (IMAC) architecture realizing both synaptic behavior and activation functions within non-volatile memory arrays. Spin-orbit torque magnetoresistive random-access memory (SOT-MRAM)…

In-memory computing promises to overcome the von Neumann bottleneck in computer systems by performing computations directly within the memory. Previous research has suggested using Spin-Transfer Torque RAM (STT-RAM) for in-memory computing…

This paper presents an in-memory computing (IMC) architecture developed on an 8x8 array of 8T SRAM cells. This architecture enables both multi-bit parallel Multiply-Accumulate (MAC) operations and standard memory processing through…

A new class of spin-transfer torque magnetic random access memory (STT-MRAM) is discussed, in which writing is achieved using thermally initiated magnonic current pulses as an alternative to conventional electric current pulses. The…

In this work, we present a novel non-volatile spin transfer torque (STT) assisted spin-orbit torque (SOT) based ternary content addressable memory (TCAM) with 5 transistors and 2 magnetic tunnel junctions (MTJs). We perform a comprehensive…

In this paper, we demonstrate the design of efficient and high-performance AI/Deep Learning accelerators with customized STT-MRAM and a reconfigurable core. Based on model-driven detailed design space exploration, we present the design…

While general-purpose computing follows Von Neumann's architecture, the data movement between memory and processor elements dictates the processor's performance. The evolving compute-in-memory (CiM) paradigm tackles this issue by…

Spin Orbit Torque-Magnetic Random Access Memory (SOT-MRAM) is being developed as a successor to the Spin transfer torque MRAM (STT-MRAM) owing to its superior performance on the metrics of reliability and read-write speed. SOT switching of…

The performance and efficiency of running large-scale datasets on traditional computing systems exhibit critical bottlenecks due to the existing "power wall" and "memory wall" problems. To resolve those problems, processing-in-memory (PIM)…

The surge in AI usage demands innovative power reduction strategies. Novel Compute-in-Memory (CIM) architectures, leveraging advanced memory technologies, hold the potential for significantly lowering energy consumption by integrating…

Spin-orbit torque (SOT) magnetization switching of ferromagnets with large perpendicular magnetic anisotropy has a great potential for the next-generation non-volatile magnetoresistive random-access memory (MRAM). It requires a…

The discovery of spin-orbit torques (SOTs) generated through the spin Hall or Rashba effects provides an alternative write approach for magnetic random-access memory (MRAM), igniting the development of spin-orbitronics in recent years.…

Spin-orbit torque (SOT) based magnetic random access memory (MRAM) is envisioned as an emerging non-volatile memory due to its ultra-high speed and low power consumption. The field-free switching schema in SOT devices is of great interest…

Electrically controllable non-volatile magnetic memories show great potential for the replacement of semiconductor-based technologies. Recently there has been strong interest in spin-orbit torque (SOT) induced magnetization reversal due to…

Recently DRAM-based PIMs (processing-in-memories) with unmodified cell arrays have demonstrated impressive performance for accelerating AI applications. However, due to the very restrictive hardware constraints, PIM remains an accelerator…

Conventional computing paradigm struggles to fulfill the rapidly growing demands from emerging applications, especially those for machine intelligence, because much of the power and energy is consumed by constant data transfers between…

A compact, accurate, and bitwidth-programmable in-memory computing (IMC) static random-access memory (SRAM) macro, named CAP-RAM, is presented for energy-efficient convolutional neural network (CNN) inference. It leverages a novel…

Analog electronic non-volatile memories mimicking synaptic operations are being explored for the implementation of neuromorphic computing systems. Compound synapses consisting of ensembles of stochastic binary elements are alternatives to…

Computing-in-memory (CIM) is proposed to alleviate the processor-memory data transfer bottleneck in traditional Von-Neumann architectures, and spintronics-based magnetic memory has demonstrated many facilitation in implementing CIM…