Name
Title | ||
|---|---|---|
Demystifying Compression Techniques in CNNs: CPU, GPU and FPGA cross-platform analysis |
Abstract | ||
|---|---|---|
Convolutional Neural Networks (CNNs) are known for their high-performance despite its huge memory requirement and computational complexity. A wide range of compression techniques to reduce the number of parameters and hence computational and memory complexity have been exploring recently. In this paper, we analyse three widely used categories of techniques viz. quantization, pruning and tensor dec... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/VLSID51830.2021.00046 | 2021 34th International Conference on VLSI Design and 2021 20th International Conference on Embedded Systems (VLSID) |
Keywords | DocType | ISSN |
Performance evaluation,Quantization (signal),Tensors,Computational modeling,Memory management,Graphics processing units,Very large scale integration | Conference | 1063-9667 |
ISBN | Citations | PageRank |
978-1-6654-4087-5 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Remya Ramakrishnan | 1 | 0 | 0.34 |
| Aditya K V Dev | 2 | 0 | 0.34 |
| A S Darshik | 3 | 0 | 0.34 |
| Renuka Chinchwadkar | 4 | 0 | 0.34 |
| Madhura Purnaprajna | 5 | 0 | 1.69 |