From the discovery of giant magnetoresistance (GMR) to tunnel magnetoresistance (TMR),
their subsequent application in large capacity hard disk drives (HDDs) greatly speeded up …

There is increasing demand to bring machine learning capabilities to low power devices. By
integrating the computational power of machine learning with the deployment capabilities of …

Neural networks span a wide range of applications of industrial and commercial
significance. Binary neural networks (BNN) are particularly effective in trading accuracy for …

Conventional von Neumann architecture suffers from data trafficking and power hungriness
between memory and processing units. Recent architectures escalating to overcome these …

The conventional computing paradigm struggles to fulfill the rapidly growing demands from
emerging applications, especially those for machine intelligence because much of the …

Processing-in-Memory (PIM) architectures have gained popularity due to their ability to
alleviate the memory wall by performing large numbers of operations within the memory …

Digital processing-in-memory (PIM) architectures are rapidly emerging to overcome the
memory-wall bottleneck by integrating logic within memory elements. Such architectures …

In the era of big data, the memory wall between the processor and the memory as well as
leakage current have become major bottlenecks of the traditional CMOS-based Von …

Recently proposed probabilistic spin logic (PSL) has offered promising solutions to novel
computing applications, including some that have previously been covered by quantum …

High resolution Fast Fourier Transform (FFT) is important for various applications while
increased memory access and parallelism requirement limits the traditional hardware. In this …