Name
Abstract | ||
|---|---|---|
Unlike general purpose computer architectures that are comprised of complex processor cores and sequential computation, the brain is innately parallel and contains highly complex connections between computational units (neurons). Key to the architecture of the brain is a functionality enabled by the combined effect of spiking communication and sparse connectivity with unique variable efficacies and temporal latencies. Utilizing these neuroscience principles, we have developed the Spiking Temporal Processing Unit (STPU) architecture which is well-suited for areas such as pattern recognition and natural language processing. In this paper, we formally describe the STPU, implement the STPU on a field programmable gate array, and show measured performance data. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICRC.2017.8123631 | 2017 IEEE International Conference on Rebooting Computing (ICRC) |
Keywords | DocType | ISBN |
computational units,brain,combined effect,spiking communication,sparse connectivity,unique variable efficacies,temporal latencies,neuroscience principles,Spiking Temporal Processing Unit architecture,STPU,natural language processing,spike-timing neuromorphic architecture,general purpose computer architectures,complex processor cores,sequential computation,performance data,field programmable gate array,highly complex connections | Conference | 978-1-5386-1554-6 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
13 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aaron J. Hill | 1 | 0 | 0.34 |
| Jonathon W. Donaldson | 2 | 0 | 0.34 |
| Fredrick H. Rothganger | 3 | 2 | 1.05 |
| Craig M. Vineyard | 4 | 7 | 7.35 |
| David R. Follett | 5 | 0 | 0.34 |
| Pamela L. Follett | 6 | 0 | 0.34 |
| Michael R. Smith | 7 | 1 | 0.69 |
| Stephen J. Verzi | 8 | 27 | 7.34 |
| William Severa | 9 | 6 | 3.22 |
| Felix Wang | 10 | 1 | 1.03 |
| James B. Aimone | 11 | 15 | 10.69 |
| John H. Naegle | 12 | 0 | 0.68 |
| Conrad D. James | 13 | 1 | 1.38 |