M
Mohammad Saeed Ansari
Researcher at University of Alberta
Publications - 17
Citations - 395
Mohammad Saeed Ansari is an academic researcher from University of Alberta. The author has contributed to research in topics: Adder & Logarithm. The author has an hindex of 6, co-authored 17 publications receiving 157 citations. Previous affiliations of Mohammad Saeed Ansari include Iran University of Science and Technology.
Papers
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Journal ArticleDOI
Low-Power Approximate Multipliers Using Encoded Partial Products and Approximate Compressors
TL;DR: For the first time, the applicability and practicality of approximate multipliers in multiple-input multiple-output antenna communication systems with error control coding are shown.
Journal ArticleDOI
Improving the Accuracy and Hardware Efficiency of Neural Networks Using Approximate Multipliers
Mohammad Saeed Ansari,Vojtech Mrazek,Bruce F. Cockburn,Lukas Sekanina,Zdenek Vasicek,Jie Han +5 more
TL;DR: This article replaces the exact multipliers in two representative NNs with approximate designs to evaluate their effect on the classification accuracy and shows that using AMs can also improve the NN accuracy by introducing noise.
Journal ArticleDOI
An Improved Logarithmic Multiplier for Energy-Efficient Neural Computing
TL;DR: This article proposes an improved logarithmic multiplier (ILM) that, unlike existing designs, rounds both inputs to their nearest powers of two by using a proposed nearest-one detector (NOD) circuit.
Proceedings ArticleDOI
A Hardware-Efficient Logarithmic Multiplier with Improved Accuracy
TL;DR: This paper presents a novel method to approximate log2N that, unlike the existing approaches, rounds N to its nearest power of two instead of the highestPower of two smaller than or equal to N.
Proceedings ArticleDOI
Characterizing Approximate Adders and Multipliers Optimized under Different Design Constraints
Honglan Jiang,Francisco Javier Hernandez Santiago,Mohammad Saeed Ansari,Leibo Liu,Bruce F. Cockburn,Fabrizio Lombardi,Jie Han +6 more
TL;DR: Approximate adders and multipliers are evaluated and compared for a better understanding of their characteristics when the implementations are optimized for performance or power.