Hierarchical temporal memory (HTM) tries to mimic the computing in cerebral neocortex. It identifies spatial and temporal patterns in the input for making inferences. This may require a large number of computationally expensive tasks, such as dot product evaluations. Nanodevices that can provide direct mapping for such primitives are of great interest. In this paper, we propose that the computing blocks for HTM can be mapped using low-voltage, magnetometallic spin-neurons combined with an emerging resistive crossbar network, which involves a comprehensive design at algorithm, architecture, circuit, and device levels. Simulation results show the possibility of more than 200× lower energy as compared with a 45-nm CMOS ASIC design.
|Number of pages
|IEEE Transactions on Neural Networks and Learning Systems
|Published - Sep 2016
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Computer Networks and Communications
- Artificial Intelligence