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June 3, 20260 citationsOpen Access

Next-Generation Embedded Systems: Edge Computing, IoT and Artificial Intelligence Convergence

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PPPramod Gouda P PatilMRManjunatha Prasad R

Key Points

  • This research aims to analyze the convergence of edge computing, IoT, and AI in embedded systems design.
  • Detailed analysis of hardware-level optimizations
  • Special emphasis on sub-micron CMOS and PVT variations
  • Exploration of DSP accelerators and RTOS mathematics scheduling.
  • Achieved a 47.8% improvement in Power-Delay Product at the 45nm technology node for custom D flip-flops.

Abstract

The spread of the Internet of Things (IoT) and Artificial Intelligence at the Edge (Edge AI) has fundamentally redefined the design paradigms embedded systems. These systems have transformed themselves into a highly connected node out of the isolated single functional microcontrollers. Usually requiring real-time deterministic processing power, area and thermal factor requirement. This paper examines the overlap between the principles of Very-Large-Scale Integration (VLSI) design and the current embedded computing architectures. Our multi-layered, detailed analysis of hardware-level optimizations with special emphasis on. sub-micron CMOS corner analysis of Process, Voltage and Temperature (PVT) variations and the use of transmission gate (TG) logic to convert sequential elements to power-efficient versions. Implementation of 3- gating. Transmission gate (TG) logic of sequential elements. In addition, this paper explores the accelerator of Digital Signal Processing (DSP), mathematics scheduling of Operating Systems (RTOS) Real-Time, and why Universal Verification is required. Procedure (UVM) to make things work. A 47.8% higher improvement in the indicates numerical simulations at the 45nm technology node. Power-Delay Product (PDP) of custom D flip-flops, laying a strong physical groundwork for next-gen intelligent edge devices.

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Cite This Study

Patil et al. (2026) studied this question.

synapsesocial.com/papers/6a1fc696dee9eb8c0dce7993https://doi.org/10.5281/zenodo.20487302
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