Name
Abstract | ||
|---|---|---|
Electrical static random memory (E-SRAM) is the current standard for internal static memory in Field Programmable Gate Array (FPGA). Despite the dramatic improvement in E-SRAM technology over the past decade, the goal of ultra-fast, energy-efficient static random memory has yet to be achieved with E-SRAM technology. However, preliminary research into optical static random access memory (O-SRAM) has shown promising results in creating energy-efficient ultra-fast static memories. This paper investigates the advantage of O-SRAM over E-SRAM in access speed and energy performance while exe-cuting sparse Matricized Tensor Times Khatri-Rao Product (spMTTKRP). spMTTKRP is an essential component of tensor decomposition algorithms which is heavily used in data science applications. The evaluation results show O-SRAMs can achieve speeds of 1.1 × − 2.9 × while saving 2.8 × − 8.1 × energy compared to conventional E-SRAM technology. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/HPEC55821.2022.9926407 | 2022 IEEE High Performance Extreme Computing Conference (HPEC) |
Keywords | DocType | ISSN |
Optical Static Random Access Memory,energy efficiency,spMTTKRP,Memory Systems,FPGA,Tensor Decom-position | Conference | 2377-6943 |
ISBN | Citations | PageRank |
978-1-6654-9787-9 | 0 | 0.34 |
References | Authors | |
6 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sasindu Wijeratne | 1 | 0 | 0.34 |
| Akhilesh Jaiswal | 2 | 0 | 0.34 |
| Ajey P. Jacob | 3 | 0 | 0.34 |
| Bingyi Zhang | 4 | 0 | 0.34 |
| Viktor K. Prasanna | 5 | 7211 | 762.74 |