Sina Shahhosseini

TL;DR: A smart and self-aware system capable of adaptively making a decision at run-time in response to the changes in pain level and context can minimize energy consumption by dynamically offloading tasks to the gateway devices at the edge layer.

TL;DR: This paper proposes a static approach to find the optimal computation migration strategy using models known at design-time and proposes a dynamic approach which can adaptively identify the latency optimal computation layer at runtime.

TL;DR: The impact of the computation offloading on total application response time in three-layer IoT systems considering more realistic parameters, e.g., application characteristics, system complexity, communication cost, and dataflow configuration is explored.

TL;DR: These explorations help designers select the right PQC implementations for making future signal processing applications quantum-resistant, as well as design two high-throughput systolic array polynomial multipliers, including NTT-based and convolution-based, and compare them to the low-cost sequential (non-systolic) N TT-based multiplier.

TL;DR: In this article, the authors explore the energy efficiency of polynomial multiplier using systolic architecture for the first time, and design two high-throughput Systolic array Polynomial multipliers, including NTT-based and convolution-based, and compare them to their low-cost sequential (non-systolic) NTTbased multiplier.