Mendeley
TY  _  JOUR
ID  -  1401101340662
TI  -  Multi-level ternary quantization for improving sparsity and computation in embedded deep neural networks
JO  -  Journal of Information and Communication Technology
JA  -  ES
LA  -  fa
SN  -  2717-0411
AU  -  Manavi Mofrad Hosna
AU  -  ansarmohammadi ali
AU  -  Salehi Mostafa
AD  -  دانشجو
AD  -  دانشجو مقطع دکتری، دانشکده مهندسی برق و کامپیوتر، دانشگاه تهران، تهران، ایران
AD  -  دانشگاه تهران
Y1  -  2024
PY  -  2024
VL  -  59
IS  -  
SP  -  125
EP  -  143
KW  -  deep neural networks
KW  -   multi-level ternary quantization
KW  -   sparse neural network
KW  -   pruning
KW  -   embedded devices
DO  -  
N2  -  Deep neural networks (DNNs) have achieved great interest due to their success in various applications. However, the computation complexity and memory size are considered to be the main obstacles for implementing such models on embedded devices with limited memory and computational resources. Network compression techniques can overcome these challenges. Quantization and pruning methods are the most important compression techniques among them. One of the famous quantization methods in DNNs is the multi-level binary quantization, which not only exploits simple bit-wise logical operations, but also reduces the accuracy gap between binary neural networks and full precision DNNs. Since, multi-level binary can’t represent the zero value, this quantization does’nt take advantage of sparsity. On the other hand, it has been shown that DNNs are sparse, and by pruning the parameters of the DNNs, the amount of data storage in memory is reduced while computation speedup is also achieved. 
In this paper, we propose a pruning and quantization-aware training method for multi-level ternary quantization that takes advantage of both multi-level quantization and data sparsity. In addition to increasing the accuracy of the network compared to the binary multi-level networks, it gives the network the ability to be sparse. To save memory size and computation complexity, we increase the sparsity in the quantized network by pruning until the accuracy loss is negligible. The results show that the potential speedup of computation for our model at the bit and word-level sparsity can be increased by 15x and 45x compared to the basic multi-level binary networks.
UR  - rimag.ir/en/Article/40662
L1  - rimag.ir/en/Article/Download/40662
ER  -  
