最新报道：zkCuda will continue to move towards a universal proof framework that is efficient, highly scalable, and highly adaptable. Original author: Zhiyong Fang In recent years, machine learning models have achieved leapfrog development at an astonishing rate. As model capabilities have increased, their complexity has also increased dramatically - today's advanced models often contain millions or even billions of parameters. In response to such scale challenges, a variety of zero-knowledge proof systems have emerged, which are always committed to achieving a dynamic balance between proof time, verification time, and proof size. The number of parameters in modern machine learning models is often in the billions, which already occupies extremely high memory resources even without any cryptographic processing. In the context of zero-knowledge proof (ZKP), this challenge is further amplified. Each floating-point parameter must be converted to an element in the algebraic domain, and this conversion process itself will increase memory usage by about 5 to 10 times. In addition, in order to accurately simulate floating-point operations in the algebraic domain, additional operation overhead must be introduced, which is usually also around 5 times. In general, the overall memory requirement of the model may increase to 25 to 50 times the original size. For example, a model with 1 billion 32-bit floating-point parameters may require 100 to 200 GB of memory just to store the converted parameters. Considering the overhead of intermediate calculation values and the proof system itself, the overall memory usage easily exceeds the TB level. Current mainstream proof systems, such as Groth16 and Plonk, usually assume that all relevant data can be loaded into memory at the same time in unoptimized implementations. Although this assumption is technically feasible, it is extremely challenging under actual hardware conditions and greatly limits the available proof computing resources. Polyhedra's zkCUDA is a zero-knowledge computing environment for high-performance circuit development, designed to improve the efficiency of proof generation. The zkCUDA language is highly similar to CUDA in syntax and semantics, and is implemented in Rust at the bottom layer to ensure both security and performance. With zkCUDA, developers can quickly build high-performance ZK circuits; efficiently schedule and utilize distributed hardware resources, such as GPUs or cluster environments that support MPI, to achieve large-scale parallel computing. zkCUDA supports fine-grained analysis of each computing kernel and matches it with the most suitable zero-knowledge proof system, such as GKR and Groth16, to maximize the performance advantages of various ZK protocols. It can also intelligently schedule resources to achieve heterogeneous computing task distribution between CPUs, GPUs, and FPGAs, significantly improving system performance. zkCUDA is highly compatible with the GKR protocol in terms of architecture, connecting sub-computing kernels through a polynomial commitment mechanism to ensure system completeness. GKR allows the verification of computational correctness to be recursively traced back to the input, similar to gradient backpropagation in machine learning, improving cross-kernel verification efficiency. At present, the zkCuda framework has completed initial development and has been successfully tested in multiple scenarios. In the future, technologies such as memory optimization scheduling and computational graph-level optimization will be introduced to continuously improve system performance and adaptation flexibility, and move towards a general proof framework with high efficiency, high scalability, and high adaptability.