597 citations · 4.3k across the 121 of their papers we have counts for
7 papers · 2 filters
Spin-Torque Sensors for Energy Efficient High Speed Long Interconnects
Zubair Al Azim, Abhronil Sengupta, Syed Shakib Sarwar +1
In this paper, we propose a Spin-Torque (ST) based sensing scheme that can enable energy efficient multi-bit long distance interconnect architectures. Current-mode interconnects ha…
Energy Efficient and High Performance Current-Mode Neural Network Circuit using Memristors and Digitally Assisted Analog CMOS Neurons
Aranya Goswamy, Sagar Kumashi, Vikash Sehwag +4
Emerging nano-scale programmable Resistive-RAM (RRAM) has been identified as a promising technology for implementing brain-inspired computing hardware. Several neural network archi…
High Sensitivity Biosensor using Injection Locked Spin Torque Nano-Oscillators
Tathagata Srimani, Bibhas Manna, Anand Kumar Mukhopadhyay +2
With ever increasing research on magnetic nano systems it is shown to have great potential in the areas of magnetic storage, biosensing, magnetoresistive insulation etc. In the fie…
Short-Term Plasticity and Long-Term Potentiation in Magnetic Tunnel Junctions: Towards Volatile Synapses
Abhronil Sengupta, Kaushik Roy
Synaptic memory is considered to be the main element responsible for learning and cognition in humans. Although traditionally non-volatile long-term plasticity changes have been im…
Proposal for an All-Spin Artificial Neural Network: Emulating Neural and Synaptic Functionalities Through Domain Wall Motion in Ferromagnets
Abhronil Sengupta, Yong Shim, Kaushik Roy
Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neurom…
Magnetic Tunnel Junction Mimics Stochastic Cortical Spiking Neurons
Abhronil Sengupta, Priyadarshini Panda, Parami Wijesinghe +2
Brain-inspired computing architectures attempt to mimic the computations performed in the neurons and the synapses in the human brain in order to achieve its efficiency in learning…