<div>Neuromorphic computing has gained tremendous interest because of its ability to overcome the limitations of traditional signal processing algorithms in data intensive applications such as image recognition, video analytics, or language translation. The new computing paradigm is built with the goal of achieving high energy efficiency, comparable to biological systems.</div><div>To achieve such energy efficiency, there is a need to explore new neuro-mimetic devices, circuits, and architecture, along with new learning algorithms. To that effect, we propose two main approaches:</div><div><br></div><div>First, we explore an energy-efficient hardware implementation of a bio-plausible Spiking Neural Network (SNN). The key highlights of our proposed system for SNNs are 1) addressing connectivity issues arising from Network On Chip (NOC)-based SNNs, and 2) proposing stochastic CMOS binary SNNs using biased random number generator (BRNG). On-chip Power Line Communication (PLC) is proposed to address the connectivity issues in NOC-based SNNs. PLC can use the on-chip power lines augmented with low-overhead receiver and transmitter to communicate data between neurons that are spatially far apart. We also propose a CMOS '<i>stochastic-bit</i>' with on-chip stochastic Spike Timing Dependent Plasticity (sSTDP) based learning for memory-compressed binary SNNs. A chip was fabricated in 90 nm CMOS process to demonstrate memory-efficient reconfigurable on-chip learning using sSTDP training. </div><div><br></div><div>Second, we explored coupled oscillatory systems for distance computation and convolution operation. Recent research on nano-oscillators has shown the possibility of using coupled oscillator networks as a core computing primitive for analog/non-Boolean computations. Spin-torque oscillator (STO) can be an attractive candidate for such oscillators because it is CMOS compatible, highly integratable, scalable, and frequency/phase tunable. Based on these promising features, we propose a new coupled-oscillator based architecture for hybrid spintronic/CMOS hardware that computes multi-dimensional norm. The hybrid system composed of an array of four injection-locked STOs and a CMOS detector is experimentally demonstrated. Energy and scaling analysis shows that the proposed STO-based coupled oscillatory system has higher energy efficiency compared to the CMOS-based system, and an order of magnitude faster computation speed in distance computation for high dimensional input vectors.</div>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12273074 |
| Date | 08 May 2020 |
| Creators | Minsuk Koo (8815964) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Energy_Efficient_Neuromorphic_Computing_Circuits_Interconnects_and_Architecture/12273074 |
Page generated in 0.0022 seconds