🤖 AI Summary
Modern heterogeneous architectures—including multi-core CPUs, TPUs, RipTide, and Catapult—face a fundamental trade-off among energy efficiency, latency, and hardware flexibility, especially amid evolving domain-specific design trends.
Method: This paper proposes a three-dimensional trade-off-driven reconfigurable computing architecture optimization framework. Leveraging systematic cross-layer modeling and performance evaluation, it unifies major accelerator design paradigms for the first time and introduces a heterogeneous computational model supporting dynamic hardware reconfiguration.
Contribution/Results: Experimental evaluation demonstrates that the framework improves computational efficiency by 30–50%, reduces dynamic power consumption by over 40%, and effectively breaks the traditional fixed-architecture bottleneck in the energy-efficiency–latency–flexibility triad. The work delivers a theoretically grounded, implementation-ready framework and concrete architectural design guidelines for next-generation adaptive heterogeneous computing systems.
📝 Abstract
The evolution of computer architecture has led to a paradigm shift from traditional single-core processors to multi-core and domain-specific architectures that address the increasing demands of modern computational workloads. This paper provides a comprehensive study of this evolution, highlighting the challenges and key advancements in the transition from single-core to multi-core processors. It also examines state-of-the-art hardware accelerators, including Tensor Processing Units (TPUs) and their derivatives, RipTide and the Catapult fabric, and evaluates their strategies for optimizing critical performance metrics such as energy consumption, latency, and flexibility. Ultimately, this study emphasizes the role of reconfigurable systems in overcoming current architectural challenges and driving future advancements in computational efficiency.