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Author

Vassilis Alimisis

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Aug 2026

A Hybrid OR-MUX Lower-Part Approximate Adder for Energy-Efficient Edge AI Inference on FPGA

Approximate computing has become a prominent design paradigm for energy-constrained edge AI hardware, where relaxed arithmetic precision enables significant gains in speed, area, and power. This paper presents a novel $n$-bit Hybrid OR-MUX Lower-Part Approximate Adder (HOMAxA-1) that divides operands into an approximat...

Sagar Bhivsane, Vassilis Alimisis, P. Sotiriadis et al. · 0 citations
Open access Aug 2026

Low-power analog integrated implementation of the learning vector quantization algorithm for interdisciplinary applications

A new, energy-efficient, fully analog integrated architecture of the Learning Vector Quantization algorithm, utilizing a 90nm CMOS process, employing the Cadence IC Suite for both the schematic and physical design stages.

Vassilis Alimisis, E. Serlis, Georgios Gennis et al. · 0 citations

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