Dr Mohammed Riyaz Ahmed Associate Professor

Beyond CMOS,Neuromorphic Computing, Memristor, Spintronics

Neuromorphic Engineering

The main conception is Neuromorphic engineering assumes that the dynamic behaviour of brain is mainly because of physical structure of it, whose behaviour equivalent is mimicked by physical proporties of silicon transistors. This hardware implementation enable us to go beyond simulation and implement real-time neural systems. It is not only consumes less power but also it aids us in developing technology to exploit the full capability of brain models in artificial systems.

Introduction:

Memristors can store charge during the training process and respond in a desired manner, electronically mimicking synapse behaviour. The plasticity is achieved by variation in memristance based on the temporal coincidences of pre and post-synaptic spikes.The properties of memristor such as high density, non-volatility, and recording historic behaviour of current profile with energy efficiency (identical to biological systems) makes it unique and paves a way for memristor-based neuromorphic computing architectures. The use of Memristor to realise synaptic activity provides even further surprising properties in analog CMOS Neuromorphic design.

People:

B U V Prashanth , PhD Student, 2018- Present, Sprintronics

Syed Aslam , UG Student, 2013- 2017, STDP Learning

Vidya , UG Student, 2014- 2018, Memristor

Prahlad , UG Student, 2015- 2019, Memristive Antenna

Bhoomica C M , UG Student, 2016- 2020, Memristive Oscillator

Abhinandan , UG Student, 2016- 2020, Memristive RF Switch

Anjana , UG Student, 2016- 2020, Neuromorphic Vision Systems

Publications:

S. Vidya and M. R. Ahmed, "Advent of memristor based synapses on neuromorphic engineering," 2017 International conference on Microelectronic Devices, Circuits and Systems (ICMDCS), Vellore, 2017, pp. 1-6. doi: 10.1109/ICMDCS.2017.8211706

P. J. Srinidhi, T. R. Yashaswini, N. Uttunga, S. A. Ali and M. R. Ahmed, "Implementation of STDP based learning rule in neuromorphic CMOS circuits," 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, 2017, pp. 1105-1110. doi: 10.1109/ICCONS.2017.8250637

Patent:

Work Under progress

RFID module for 5G in NS3,
Intelligent beam forming techniques,

Projects:

Beam forming

Training based beam forming

NS3 modules for 5G communication

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