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Architectural Shift Necessitated by Voracious Energy Demands of Generative Artificial Intelligence (GAI) and High-Performance Computing

 

 

1M L Sharma, 2Sunil Kumar, 3Ajay Kumar Garg, 4Mahir Pandey, 5Parth Shukla

1,2,3Faculty, Maharaja Agrasen Institute of Technology, Delhi

4,5Research Scholar, Maharaja Agrasen Institute of Technology, Delhi
1madansharma.20@gmail.com, 2sunilkumar@mait.ac.in, 3ajaygargiitr@gmail.com, 4mahirpandey2024@gmail.com, 5parthshuklaji69@gmail.com

 

 

Abstract

The computational substrate of the 21st century is undergoing a radical phase transition. The deterministic certainty that defined the era of Moore’s Law—where performance gains were achieved through the reliable shrinking of transistors without a penalty in power density—has irrevocably collapsed. As the semiconductor industry confronts the breakdown of Dennard scaling and the physical limits of lithography, a new paradigm has emerged: Approximate Computing (AC). This architectural shift, necessitated by the voracious energy demands of generative artificial intelligence and high-performance computing, deliberately trades bit-level precision for gains in energy efficiency and throughput. However, this transition from exactitude to approximation is not merely a technical optimization; it is a profound reordering of the sociotechnical contract between human operators and machine agents.

This report, "Accountable Approximation," provides an exhaustive analysis of the implications of this shift. By synthesizing data from energy audits, hardware security research, legal theory, and the geometry of neural loss landscapes, we demonstrate that the introduction of stochastic error into the hardware layer possesses significant, yet largely unexamined, agency. We explore how quantization noise—the arithmetic distortion introduced by reducing numerical precision—interacts with the high-dimensional geometry of deep learning models to disproportionately erode the representation of minority data, effectively embedding bias into the silicon itself. Furthermore, we examine the security paradox where the "fog of error" sanctioned by approximation creates a camouflage for hardware Trojans, rendering traditional redundancy-based detection methods obsolete.

Synthesizing the latest findings from the International Energy Agency (IEA), Google’s 2025 environmental reports, and cutting-edge research into "Fair-GPTQ" algorithms, this report argues that the sustainability of the AI revolution hinges on our ability to govern this new "technological unconscious." We propose a framework of Accountable Approximation that demands transparency in error budgets, rigorous auditing of the bias-variance trade-off in hardware, and a modernization of liability laws to address the non-deterministic nature of future computing systems. The era of the perfect machine is over; the era of the accountable machine must begin.

Keywords
Approximate computing, Thermodynamic computing, Hessian Spectrum Analysis, Large language models, Fair-GPTQ, Post-Moore's Law computing, Gradient Normal Disparity

 

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