PMID- 35345797
OWN - NLM
STAT- MEDLINE
DCOM- 20220330
LR  - 20220401
IS  - 1687-5273 (Electronic)
IS  - 1687-5265 (Print)
VI  - 2022
DP  - 2022
TI  - Power and Area Efficient Cascaded Effectless GDI Approximate Adder for 
      Accelerating Multimedia Applications Using Deep Learning Model.
PG  - 3505439
LID - 10.1155/2022/3505439 [doi]
LID - 3505439
AB  - Approximate computing is an upsurging technique to accelerate the process through 
      less computational effort while keeping admissible accuracy of error-tolerant 
      applications such as multimedia and deep learning. Inheritance properties of the 
      deep learning process aid the designer to abridge the circuitry and also to 
      increase the computation speed at the cost of the accuracy of results. High 
      computational complexity and low-power requirement of portable devices in the 
      dark silicon era sought suitable alternate for Complementary Metal Oxide 
      Semiconductor (CMOS) technology. Gate Diffusion Input (GDI) logic is one of the 
      prompting alternatives to CMOS logic to reduce transistors and low-power design. 
      In this work, a novel energy and area efficient 1-bit GDI-based full swing Energy 
      and Area efficient Full Adder (EAFA) with minimum error distance is proposed. The 
      proposed architecture was constructed to mitigate the cascaded effect problem in 
      GDI-based circuits. It is proved by extending the proposed 1-bit GDI-based adder 
      for different 16-bit Energy and Area Efficient High-Speed Error-Tolerant Adders 
      (EAHSETA) segmented as accurate and inaccurate adder circuits. The proposed 
      adder's design metrics in terms of delay, area, and power dissipation are 
      verified through simulation using the Cadence tool. The proposed logic is 
      deployed to accelerate the convolution process in the Low-Weight Digit Detector 
      neural network for real-time handwritten digit classification application as a 
      case study in the Intel Cyclone IV Field Programmable Gate Array (FPGA). The 
      results confirm that our proposed EAHSETA occupies fewer logic elements and 
      improves operation speed with the speed-up factor of 1.29 than other similar 
      techniques while producing 95% of classification accuracy.
CI  - Copyright (c) 2022 Manikandan Nagarajan et al.
FAU - Nagarajan, Manikandan
AU  - Nagarajan M
AUID- ORCID: 0000-0003-2031-8940
AD  - School of Computing, SASTRA Deemed University, Thanjavur 613 401, India.
FAU - Muthaiah, Rajappa
AU  - Muthaiah R
AUID- ORCID: 0000-0002-6659-1961
AD  - School of Computing, SASTRA Deemed University, Thanjavur 613 401, India.
FAU - Teekaraman, Yuvaraja
AU  - Teekaraman Y
AUID- ORCID: 0000-0003-4297-3460
AD  - Department of Electronic and Electrical Engineering, The University of Sheffield, 
      Sheffield S1 3JD, UK.
FAU - Kuppusamy, Ramya
AU  - Kuppusamy R
AUID- ORCID: 0000-0002-1249-906X
AD  - Department of Electrical and Electronics Engineering, Sri Sairam College of 
      Engineering, Bangalore 562 106, India.
FAU - Radhakrishnan, Arun
AU  - Radhakrishnan A
AUID- ORCID: 0000-0003-3700-2491
AD  - Faculty of Electrical & Computer Engineering, Jimma Institute of Technology, 
      Jimma University, Jimma, Ethiopia.
LA  - eng
PT  - Journal Article
DEP - 20220319
PL  - United States
TA  - Comput Intell Neurosci
JT  - Computational intelligence and neuroscience
JID - 101279357
SB  - IM
MH  - Computer Simulation
MH  - *Deep Learning
MH  - Diffusion
MH  - *Multimedia
MH  - Semiconductors
PMC - PMC8957425
COIS- The authors declare that they have no conflicts of interest.
EDAT- 2022/03/30 06:00
MHDA- 2022/03/31 06:00
PMCR- 2022/03/19
CRDT- 2022/03/29 05:14
PHST- 2022/01/10 00:00 [received]
PHST- 2022/02/03 00:00 [revised]
PHST- 2022/02/10 00:00 [accepted]
PHST- 2022/03/29 05:14 [entrez]
PHST- 2022/03/30 06:00 [pubmed]
PHST- 2022/03/31 06:00 [medline]
PHST- 2022/03/19 00:00 [pmc-release]
AID - 10.1155/2022/3505439 [doi]
PST - epublish
SO  - Comput Intell Neurosci. 2022 Mar 19;2022:3505439. doi: 10.1155/2022/3505439. 
      eCollection 2022.