Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of
attention lately due to its promise of reducing the computational energy, latency, as well as …

Memristor crossbars are circuits capable of performing analog matrix-vector multiplications,
overcoming the fundamental energy efficiency limitations of digital logic. They have been …

Deep neural networks (DNNs) achieve best-known accuracies in many machine learning
tasks involved in image, voice, and natural language processing and are being used in an …

The use of deep learning (DL) to real-world applications, such as computer vision, speech
recognition, and robotics, has become ubiquitous. This can be largely attributed to a virtuous …

This overview article describes Magnetoresistive Random Access Memory (MRAM) from a
circuits and systems perspective. We discuss various tradeoffs and design challenges of …

From the little we know about the human brain, the inherent cognitive mechanism is very
different from the de facto state-of-the-art computing platforms. The human brain uses …

We present a new spin-transfer torque magnetic random access memory (STT MRAM) bit-
cell that is embedded with read-only memory (ROM) functionality. The proposed ROM …

SRAM is associated with cache memory inside computer system, it increases overall speed
of the system. In this work 64kb SRAM is synthesized and simulated on Artix-7 FPGA board …

We propose a design technique for embedding read-only memory (ROM) in spin-transfer
torque MRAM (STT-MRAM) arrays by adding an extra bit-line in every column of the array …

This Paper presents an exhaustive review on BIST technique used in different fault tolerant
systems and also consider the fault detection methodologies used in these systems. BIST is …