Abstract
This short article explains why the Epiphany architecture is a proper refer-
ence for digital large-scale neuromorphic design. We compare the Epiphany
architecture with several modern digital neuromorphic processors. We show
the result of mapping the binary LeNet-5 neural network into few modern
neuromorphic architectures and demonstrate the efficient use of memory in
Epiphany. Finally, we show the results of our benchmarking experiments
with Epiphany and propose a few suggestions to improve the architecture
for neuromorphic applications. Epiphany can update a neuron on average in
120ns which is enough for many real-time neuromorphic applications.
ence for digital large-scale neuromorphic design. We compare the Epiphany
architecture with several modern digital neuromorphic processors. We show
the result of mapping the binary LeNet-5 neural network into few modern
neuromorphic architectures and demonstrate the efficient use of memory in
Epiphany. Finally, we show the results of our benchmarking experiments
with Epiphany and propose a few suggestions to improve the architecture
for neuromorphic applications. Epiphany can update a neuron on average in
120ns which is enough for many real-time neuromorphic applications.
| Original language | English |
|---|---|
| Title of host publication | Industrial Artificial Intelligence Technologies and Applications |
| Editors | Ovidiu Vermesan |
| Publisher | River Publishers |
| Chapter | 2 |
| Pages | 21-34 |
| Number of pages | 14 |
| ISBN (Electronic) | 9788770227902 |
| ISBN (Print) | 9788770227919 |
| DOIs | |
| Publication status | Published - Jun 2022 |
Publication series
| Name | River Publishers Series in Communications and Networking |
|---|