High-Performance Data Mapping for BNNs on PCM-based Integrated Photonics
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929230226391040 |
|---|---|
| author | Shahroodi, Taha Cardoso, Raphael Wong, Stephan Bosio, Alberto O'Connor, Ian Hamdioui, Said |
| author_facet | Shahroodi, Taha Cardoso, Raphael Wong, Stephan Bosio, Alberto O'Connor, Ian Hamdioui, Said |
| contents | State-of-the-Art (SotA) hardware implementations of Deep Neural Networks (DNNs) incur high latencies and costs. Binary Neural Networks (BNNs) are potential alternative solutions to realize faster implementations without losing accuracy. In this paper, we first present a new data mapping, called TacitMap, suited for BNNs implemented based on a Computation-In-Memory (CIM) architecture. TacitMap maximizes the use of available parallelism, while CIM architecture eliminates the data movement overhead. We then propose a hardware accelerator based on optical phase change memory (oPCM) called EinsteinBarrier. Ein-steinBarrier incorporates TacitMap and adds an extra dimension for parallelism through wavelength division multiplexing, leading to extra latency reduction. The simulation results show that, compared to the SotA CIM baseline, TacitMap and EinsteinBarrier significantly improve execution time by up to ~154x and ~3113x, respectively, while also maintaining the energy consumption within 60% of that in the CIM baseline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_17724 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | High-Performance Data Mapping for BNNs on PCM-based Integrated Photonics Shahroodi, Taha Cardoso, Raphael Wong, Stephan Bosio, Alberto O'Connor, Ian Hamdioui, Said Hardware Architecture Emerging Technologies State-of-the-Art (SotA) hardware implementations of Deep Neural Networks (DNNs) incur high latencies and costs. Binary Neural Networks (BNNs) are potential alternative solutions to realize faster implementations without losing accuracy. In this paper, we first present a new data mapping, called TacitMap, suited for BNNs implemented based on a Computation-In-Memory (CIM) architecture. TacitMap maximizes the use of available parallelism, while CIM architecture eliminates the data movement overhead. We then propose a hardware accelerator based on optical phase change memory (oPCM) called EinsteinBarrier. Ein-steinBarrier incorporates TacitMap and adds an extra dimension for parallelism through wavelength division multiplexing, leading to extra latency reduction. The simulation results show that, compared to the SotA CIM baseline, TacitMap and EinsteinBarrier significantly improve execution time by up to ~154x and ~3113x, respectively, while also maintaining the energy consumption within 60% of that in the CIM baseline. |
| title | High-Performance Data Mapping for BNNs on PCM-based Integrated Photonics |
| topic | Hardware Architecture Emerging Technologies |
| url | https://arxiv.org/abs/2401.17724 |