mlsanityData to 14 Oct 2026

Beyond Moore's Law: Harnessing the Redshift of Generative AI with Effective Hardware-Software Co-Design

arXiv 2504.06531 ↗First posted 9 Apr 2025cs.AR cs.AIcited by 1

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Abstract (arXiv)

For decades, Moore's Law has served as a steadfast pillar in computer architecture and system design, promoting a clear abstraction between hardware and software. This traditional Moore's computing paradigm has deepened the rift between the two, enabling software developers to achieve near-exponential performance gains often without needing to delve deeply into hardware-specific optimizations. Yet today, Moore's Law -- with its once relentless performance gains now diminished to incremental improvements -- faces inevitable physical barriers. This stagnation necessitates a reevaluation of the conventional system design philosophy. The traditional decoupled system design philosophy, which maintains strict abstractions between hardware and software, is increasingly obsolete. The once-clear boundary between software and hardware is rapidly dissolving, replaced by co-design. It is imperative for the computing community to intensify its commitment to hardware-software co-design, elevating system abstractions to first-class citizens and reimagining design principles to satisfy the insatiable appetite of modern computing. Hardware-software co-design is not a recent innovation. To illustrate its historical evolution, I classify its development into five relatively distinct ``epochs''. This post also highlights the growing influence of the architecture community in interdisciplinary teams -- particularly alongside ML researchers -- and explores why current co-design paradigms are struggling in today's computing landscape. Additionally, I will examine the concept of the ``hardware lottery'' and explore directions to mitigate its constraining influence on the next era of computing innovation.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 34 references that are in the map. First posted on arXiv 9 Apr 2025.

Builds on

9 of 34 references.

References · 9

  1. 2017Attention Is All You Needcited by 60,361
  2. 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenesscited by 3,673
  3. 2017In-Datacenter Performance Analysis of a Tensor Processing Unitcited by 618
  4. 2020The Hardware Lotterycited by 160
  5. 2019Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integrationcited by 63
  6. 2021FLAT: An Optimized Dataflow for Mitigating Attention Bottleneckscited by 39
  7. 2021Silent Data Corruptions at Scalecited by 23
  8. 2024A Survey: Collaborative Hardware and Software Design in the Era of Large Language Modelscited by 23
  9. 2018In-RDBMS Hardware Acceleration of Advanced Analyticscited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.