| About this Abstract |
| Meeting |
Materials Science & Technology 2020
|
| Symposium
|
Advances in Synthesis and Integration Methods for Enhanced Properties, and Applications in Emerging Nanomaterials
|
| Presentation Title |
Efficient Neuromorphic Computing Enabled by Spin-Transfer Torque: Devices, Circuits and Systems |
| Author(s) |
Abhronil Sengupta |
| On-Site Speaker (Planned) |
Abhronil Sengupta |
| Abstract Scope |
While research in designing brain-inspired algorithms have attained a stage where such Artificial Intelligence platforms are being able to outperform humans at several cognitive tasks, an often-unnoticed cost is the huge computational expenses required for running these algorithms in hardware. Bridging the computational efficiency gap necessitates the exploration of devices, circuits and architectures that provide a better match to the computational primitives of biological processing.
Recent experiments in spintronic technologies are revealing immense possibilities of implementing a plethora of neural and synaptic functionalities by single spintronic device structures that can be operated at very low terminal voltages. Leveraging insights from such experiments, I will present a multi-disciplinary perspective across the entire stack of devices, circuits and systems to envision the design of an "All-Spin" neuromorphic processor enabled with on-chip learning functionalities that can potentially achieve two to three orders of magnitude energy improvement in comparison to state-of-the-art CMOS implementations. |