标题： 用基于物理的ASIC解决计算危机 显示英文标题

标题： Solving the compute crisis with physics-based ASICs

摘要： 不断增长的人工智能(AI)需求暴露了一个关键的“计算危机”，其特点是不可持续的能耗、高昂的训练成本以及传统CMOS扩展的接近极限。 基于物理的 专用集成电路(ASICs)通过直接利用内在物理动态进行计算，而不是耗费资源来强制理想化的数字抽象，提供了一种变革性的范式。 通过放松传统ASICs所需的约束，如强制的无状态性、单向性、确定性和同步性，这些设备旨在作为物理过程的精确实现，从而在能源效率和计算吞吐量方面带来显著提升。 这种方法使新型协同设计策略成为可能，将算法需求与物理系统的固有计算原语相匹配。 基于物理的 ASICs可以加速关键的AI应用，如扩散模型、采样、优化和神经网络推理，以及传统的计算工作负载，如材料和分子的科学模拟。 最终，这一愿景指向了一个异构的、高度专业化的计算平台的未来，能够克服当前的扩展瓶颈，并解锁计算能力和效率的新前沿。 显示英文摘要

摘要： Escalating artificial intelligence (AI) demands expose a critical "compute crisis" characterized by unsustainable energy consumption, prohibitive training costs, and the approaching limits of conventional CMOS scaling. Physics-based Application-Specific Integrated Circuits (ASICs) present a transformative paradigm by directly harnessing intrinsic physical dynamics for computation rather than expending resources to enforce idealized digital abstractions. By relaxing the constraints needed for traditional ASICs, like enforced statelessness, unidirectionality, determinism, and synchronization, these devices aim to operate as exact realizations of physical processes, offering substantial gains in energy efficiency and computational throughput. This approach enables novel co-design strategies, aligning algorithmic requirements with the inherent computational primitives of physical systems. Physics-based ASICs could accelerate critical AI applications like diffusion models, sampling, optimization, and neural network inference as well as traditional computational workloads like scientific simulation of materials and molecules. Ultimately, this vision points towards a future of heterogeneous, highly-specialized computing platforms capable of overcoming current scaling bottlenecks and unlocking new frontiers in computational power and efficiency.