A quality-aware voltage overscaling framework to improve the energy efficiency and lifetime of TPUs based on statistical error modeling
Deep neural networks (DNNs) are a type of artificial intelligence models that are inspired by
the structure and function of the human brain, designed to process and learn from large …
the structure and function of the human brain, designed to process and learn from large …
HPR-Mul: An Area and Energy-Efficient High-Precision Redundancy Multiplier by Approximate Computing
For critical applications that require a higher level of reliability, the triple modular
redundancy (TMR) scheme is usually employed to implement fault-tolerant arithmetic units …
redundancy (TMR) scheme is usually employed to implement fault-tolerant arithmetic units …
Design Exploration of Fault-Tolerant Deep Neural Networks Using Posit Number Representation System
M Yousefloo, O Akbari - IEEE Transactions on Very Large Scale …, 2024 - ieeexplore.ieee.org
The applications of deep neural networks (DNNs) in different safety-critical systems (such as
autonomous vehicles and robotics) are experiencing emerging growth due to their high …
autonomous vehicles and robotics) are experiencing emerging growth due to their high …
Reconfigurable Approximating Accelerators for Edge Computing
HJ Damsgaard - 2024 - trepo.tuni.fi
Edge computing is one of the key technologies for tackling the growing rate of data
production and connectivity demands in Internet of Things (IoT) devices. Distributed edge …
production and connectivity demands in Internet of Things (IoT) devices. Distributed edge …
[引用][C] Design and Analysis of Optimized Approximate Multiplier Using Novel Higher-Order Compressor
The energy-efficient error-tolerant circuits have paved the way for a whole new area in low-
power consumption applications with approximate computing. The approximate computing …
power consumption applications with approximate computing. The approximate computing …