A Hardware/Software Co-Design Methodology for Adaptive Approximate Computing in clustering and ANN Learning
As one of the most promising energy-efficient emerging paradigms for designing digital systems, approximate computing has attracted a significant attention in recent years. Applications utilizing approximate computing (AxC) can tolerate some loss of quality in the computed results for attaining high...
Main Authors: | Pengfei Huang, Chenghua Wang, Weiqiang Liu, Fei Qiao, Fabrizio Lombardi |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Open Journal of the Computer Society |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9324944/ |
Similar Items
-
A Cost-Efficient Approximate Dynamic Ranged Multiplication and Approximation-Aware Training on Convolutional Neural Networks
by: Hyunjin Kim, et al.
Published: (2021-01-01) -
SquASH: Approximate Square-Accumulate With Self-Healing
by: G. A. Gillani, et al.
Published: (2018-01-01) -
APPROXIMATE N-NEAREST NEIGHBOR CLUSTERING ON DISTRIBUTED DATABASES USING ITERATIVE REFINEMENT
by: CALENDER, CHRISTOPHER R.
Published: (2004) -
MACISH: Designing Approximate MAC Accelerators With Internal-Self-Healing
by: G. A. Gillani, et al.
Published: (2019-01-01) -
CNN Inference Using a Preprocessing Precision Controller and Approximate Multipliers With Various Precisions
by: Issam Hammad, et al.
Published: (2021-01-01)