Hardware-Software Co-Design for Sparse and Long-Context AI Models: Architectural Strategies and Platforms

Abstract

The rapid proliferation of large-scale artificial intelligence (AI) models—particularly those with sparse architectures and extended context windows—has fundamentally transformed the relationship between software algorithms and computing hardware. Traditional accelerator designs optimized for dense matrix operations have become increasingly inefficient when faced with modern architectures such as Mixture-of-Experts (MoE), LongContext Transformers, and multimodal fusion models that demand irregular computation, massive memory bandwidth, and flexible interconnect topologies. This evolution necessitates a paradigm shift toward hardware–software co-design, where algorithmic and hardware layers are jointly optimized to achieve scalability, energy efficiency, and performance consistency across heterogeneous workloads. This paper investigates architectural strategies and platform innovations that enable co-optimization between model design and hardware implementation. We explore the computational implications of sparsity and long-context processing, analyzing how these properties drive demands on memory hierarchies, communication fabrics, and compiler frameworks. The study examines leading co-design approaches implemented in state-of-the-art AI accelerators, including NVIDIA Blackwell, Google TPU v5e, Cerebras Wafer-Scale Engine 3, and AMD MI300X, highlighting trade-offs in throughput, energy efficiency, and flexibility. Quantitative evaluations and conceptual frameworks are presented to guide future research into model-aware hardware adaptation, emphasizing the symbiotic evolution of software frameworks (e.g., PyTorch/XLA, DeepSpeed, and TVM) and hardware architectures. By aligning algorithmic sparsity patterns, attention scaling, and data movement strategies with hardware execution models, the paper demonstrates that co-design methodologies are pivotal for sustaining the exponential growth of AI model capabilities within practical energy and cost boundaries.