Name
Title | ||
|---|---|---|
Exploration of Segmented Bus As Scalable Global Interconnect for Neuromorphic Computing |
Abstract | ||
|---|---|---|
Spiking Neural Networks (SNNs) are efficient computation models for spatio-temporal pattern recognition on resource and power constrained platforms. Dedicated SNN hardware, also called neuromorphic hardware, can further reduce the energy consumption of these platforms. A neuromorphic hardware consists of crossbars, which are arrangements of input and output neurons with fully-connected synapses. Time-multiplexed interconnects are used to communicate spikes between crossbars. When a SNN model is mapped on multiple crossbars, the time-multiplexed interconnect increases spike latency and energy consumption, and disorders spike arrivals at output neurons, which reduces application accuracy. In this paper, we propose segmented bus interconnect for global synapses in a neuromorphic architecture. The objective is to reduce power consumption and enable parallel processing compared to traditional time-multiplexed interconnects. The fundamental idea for the segmented bus is to partition a single bus into several segments, with the segmentation switches controlled by software. We evaluate the scalability of segmented bus using synthetic applications. Our results show that segmented bus reduces the latency and energy consumption of the global synapse network significantly with respect to state-of-the-art techniques.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3299874.3319491 | Proceedings of the 2019 on Great Lakes Symposium on VLSI |
Keywords | Field | DocType |
segmented bus, spike latency, spiking neural networks (snns) | Computer science,Latency (engineering),Neuromorphic engineering,Real-time computing,Input/output,Software,Computer hardware,Interconnection,Spiking neural network,Energy consumption,Scalability | Conference |
ISSN | ISBN | Citations |
1066-1395 | 978-1-4503-6252-8 | 1 |
PageRank | References | Authors |
0.35 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adarsha Balaji | 1 | 15 | 4.27 |
| Yuefeng Wu | 2 | 2 | 0.73 |
| Anup Das 0001 | 3 | 367 | 33.35 |
| Francky Catthoor | 4 | 3932 | 423.30 |
| Siebren Schaafsma | 5 | 18 | 2.65 |