Name
Abstract | ||
|---|---|---|
ABSTRACTIn this paper, we present our state-of-the-art approximate techniques that cover the main pillars of approximate computing research. Our analysis considers both static and reconfigurable approximation techniques as well as operation-specific approximate components (e.g., multipliers) and generalized approximate highlevel synthesis approaches. As our application target, we discuss the improvements that such techniques bring on machine learning and neural networks. In addition to the conventionally analyzed performance and energy gains, we also evaluate the improvements that approximate computing brings in the operating temperature. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3394885.3431632 | ASPDAC |
Keywords | DocType | ISSN |
Approximate Computing, Architecture, Accelerator, High-Level Synthesis, Inference, Logic, Low-power, Multiplier, Neural Network, Renconfigurable Accuracy, Temperature | Conference | 2153-6961 |
ISBN | Citations | PageRank |
978-1-7281-8057-1 | 2 | 0.41 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Georgios Zervakis | 1 | 49 | 8.33 |
| Hassaan Saadat | 2 | 9 | 2.55 |
| Hussam Amrouch | 3 | 251 | 50.22 |
| Andreas Gerstlauer | 4 | 890 | 78.75 |
| Sri Parameswaran | 5 | 1062 | 102.76 |
| J. Henkel | 6 | 4471 | 366.50 |