Evaluation and Comparison: Benchmarking ZKML Frameworks

Zero knowledge machine learning (ZKML) represents a groundbreaking fusion of two cutting-edge domains: zero knowledge proofs (ZKPs) and machine learning (ML). At its core, ZKML embodies a revolutionary approach to ensuring the confidentiality and integrity of ML model computations, offering a paradigm shift in data privacy and security. As the proliferation of ML models accelerates across diverse industries, the demand for robust verification mechanisms that safeguard sensitive information becomes increasingly pronounced. In response to this imperative, ZKML emerges as a beacon of innovation, leveraging cryptographic protocols to provide verifiable assurances without divulging underlying data.

Within the rapidly evolving landscape of data-driven decision-making, ZKML serves as a linchpin for organizations seeking to harness the power of ML while upholding stringent privacy standards. By harnessing the principles of ZKPs, ZKML enables entities to verify the correctness of ML model outputs without necessitating the disclosure of proprietary data or model architectures. This not only bolsters trust and transparency but also mitigates the risks associated with data breaches and unauthorized access.

source: SevenX Ventures Mirror

This article based on a researched by blog.ezkl.xyz

I. ZKML Frameworks

Central to the realization of ZKML's transformative potential are the frameworks that underpin its implementation. Among the notable contenders in this domain are EZKL, RISC Zero, and Orion, each distinguished by its unique approach to ZKML deployment and functionality.

- EZKL: Positioned at the forefront of ZKML innovation, EZKL epitomizes user-friendly accessibility and robust performance. Leveraging the Halo2 framework, EZKL seamlessly converts ML models represented in the Open Neural Network Exchange (ONNX) format into zkSNARK circuits, facilitating efficient inference and verification processes.

- RISC Zero: Embodying a Rust-based architecture, RISC Zero embodies a formidable contender in the realm of ZKML frameworks. Powered by the SmartCore ML Rust crate, RISC Zero employs zkSTARK proofs within its Zero Knowledge Virtual Machine (ZKVM), offering a versatile platform for secure ML computations.

- Orion: With a focus on zkSTARKs and the Cairo programming language, Orion stands as a testament to innovation in ZKML framework design. By harnessing the expressive power of zkSTARKs and the efficiency of Cairo, Orion enables seamless conversion of ONNX models into verifiable circuits, thereby facilitating privacy-preserving ML inference.

Interested in learning more about Zero Knowledge technologies? Check out our latest paper on important concepts to understand Zero-Knowledge better in 2024.

Objective of the Benchmarking Study

Against the backdrop of burgeoning interest and adoption of ZKML frameworks, our benchmarking study endeavors to elucidate the comparative performance and efficacy of EZKL, RISC Zero, and Orion. By meticulously evaluating key metrics such as proving time, memory usage, and setup complexity, we aim to empower stakeholders with actionable insights to inform their selection and deployment of ZKML frameworks. Through rigorous experimentation and analysis, we seek to unravel the intricacies of ZKML framework dynamics, paving the way for enhanced trust, transparency, and efficiency in the realm of secure machine learning.

source: blog.ezkl.xyz

II. Methodology and Setup

In this section, we delineate the systematic methodology and setup employed in our benchmarking study, ensuring rigor and reproducibility in our evaluation of ZKML frameworks.

Benchmarking Metrics and Criteria

Central to our benchmarking methodology are meticulously defined metrics and criteria aimed at providing a comprehensive assessment of ZKML framework performance. Key metrics include proving time, memory usage, and setup complexity, each offering unique insights into the efficiency and usability of EZKL, RISC Zero, and Orion. By standardizing our evaluation criteria, we aim to facilitate meaningful comparisons and actionable decision-making for stakeholders navigating the ZKML landscape.

Setup Complexity Comparison

An integral aspect of our benchmarking study is the comparative analysis of setup complexity across EZKL, RISC Zero, and Orion. By scrutinizing the setup processes of each framework, we shed light on the user experience and deployment intricacies inherent to ZKML framework adoption. From straightforward installation procedures to nuanced configuration requirements, our analysis aims to elucidate the nuances of framework setup and empower stakeholders with insights to navigate implementation challenges effectively.

Replicability Measures

Ensuring the replicability of our benchmarking study is paramount to its validity and utility for stakeholders. To this end, we meticulously document our experimental procedures and provide detailed instructions for replicating our findings. Whether through open-access repositories or comprehensive documentation, we strive to foster an environment conducive to transparent and reproducible research in the realm of ZKML framework evaluation.

Experimental Setup

Our benchmarking experiments are conducted in controlled environments to ensure consistency and reliability in our results. Utilizing state-of-the-art hardware configurations and software environments, we aim to minimize confounding variables and isolate the performance characteristics of EZKL, RISC Zero, and Orion. By adhering to rigorous experimental protocols, we endeavor to provide stakeholders with trustworthy insights into the comparative performance of ZKML frameworks.

Data Collection and Analysis

Throughout the benchmarking process, we meticulously collect and analyze data pertaining to proving time, memory usage, and other relevant metrics. Leveraging advanced analytical tools and methodologies, we distill raw data into actionable insights, enabling stakeholders to make informed decisions regarding ZKML framework selection and deployment. Our analytical approach prioritizes objectivity and transparency, ensuring the integrity and reliability of our benchmarking findings.

Our methodology and setup lay the foundation for a robust and insightful benchmarking study of EZKL, RISC Zero, and Orion. By employing standardized metrics, scrutinizing setup complexities, and prioritizing replicability, we strive to deliver actionable insights that empower stakeholders in their journey towards secure and efficient ZKML implementation. Through transparency, rigor, and diligence, we endeavor to advance the collective understanding of ZKML framework dynamics and foster innovation in the realm of secure machine learning.

III. Benchmarking Results

In this section, we present the empirical findings derived from our comparative analysis of EZKL, RISC Zero, and Orion, focusing on key metrics such as proving time and memory usage across various model types.

Comparative Analysis

Our benchmarking endeavors unveil striking differentials in performance metrics among EZKL, RISC Zero, and Orion, underscoring the nuanced dynamics of ZKML framework efficacy.

Proving Time

Across all model types evaluated, EZKL consistently demonstrates remarkable efficiency in proving time compared to RISC Zero and Orion. The streamlined architecture of EZKL, leveraging the Halo2 framework, facilitates rapid inference and verification processes, thereby reducing proving times significantly. Conversely, RISC Zero and Orion exhibit comparatively longer proving times, attributable to inherent complexities in their zkSTARK-based and Cairo-based implementations, respectively.

Memory Usage

In terms of memory usage, EZKL outshines its counterparts, exhibiting superior efficiency across all model types tested. The optimized memory management strategies employed within EZKL result in significantly lower memory footprints compared to RISC Zero and Orion. This efficiency is particularly pronounced in scenarios involving large-scale model computations, where memory constraints pose significant challenges.

Model-Specific Performance

Delving deeper into model-specific performance, we observe nuanced variations in proving time and memory usage across linear regression, random forest classification, SVM classification, and tree ensemble regression models. EZKL consistently outperforms RISC Zero and Orion across all model types, reaffirming its dominance in ZKML framework efficiency and efficacy.

Aggregate Performance Comparison

Aggregating our findings across model types, EZKL emerges as the clear frontrunner, boasting superior performance metrics in terms of proving time and memory usage. On average, EZKL exhibits a significant edge over RISC Zero and Orion, underscoring its prowess as a leading ZKML framework for secure and efficient ML computations.

Our benchmarking results provide compelling evidence of the performance differentials among EZKL, RISC Zero, and Orion in the realm of ZKML. By elucidating the nuanced dynamics of proving time and memory usage across various model types, we offer stakeholders invaluable insights into the efficacy and efficiency of ZKML frameworks. As organizations navigate the complexities of ZKML adoption, our benchmarking findings serve as a compass, guiding the selection and deployment of frameworks that align with their specific needs and objectives. Through transparency, rigor, and diligence, we strive to advance the collective understanding of ZKML framework dynamics and catalyze innovation in the domain of secure machine learning.

III. Analysis and Insights

In this section, we delve into a nuanced examination of the setup experiences, technical intricacies, and implications for model accuracy within the context of EZKL, RISC Zero, and Orion ZKML frameworks.

Setup Experiences

The setup processes for EZKL, RISC Zero, and Orion offer distinct user experiences, each with its own set of advantages and challenges. EZKL distinguishes itself with its user-friendly setup, facilitated by comprehensive documentation and streamlined deployment procedures. In contrast, RISC Zero presents a more involved setup process, requiring users to navigate Rust-based environments and orchestrate host-guest program configurations. Similarly, Orion's setup entails installation of scarb and negotiation of Cairo programming nuances, introducing additional complexity.

Technical Aspects

Technical nuances play a pivotal role in shaping the performance differentials observed among EZKL, RISC Zero, and Orion. EZKL's utilization of the Halo2 framework, coupled with efficient einsum operations and logUP lookup table arguments, contributes to its exceptional performance in proving time and memory usage. Conversely, RISC Zero and Orion leverage zkSTARKs and Cairo, respectively, introducing overheads associated with virtual machine-based computations and complex programming paradigms.

Implications for Model Accuracy

Beyond performance metrics, considerations of model accuracy and quantization strategies emerge as critical factors influencing ZKML framework selection. RISC Zero's internal handling of quantization and fixed-point scaling may introduce precision trade-offs, potentially impacting model accuracy. In contrast, EZKL empowers users with adjustable precision in quantization, enabling fine-tuning of accuracy-performance trade-offs. Orion presents a similar approach, albeit with limitations in certain model architectures, as evidenced by out-of-memory errors encountered during benchmarking.

Our analysis sheds light on the multifaceted dynamics shaping the performance and usability of EZKL, RISC Zero, and Orion ZKML frameworks. By dissecting setup experiences, exploring technical intricacies, and unraveling implications for model accuracy, we offer stakeholders actionable insights to inform their framework selection and deployment strategies. As organizations navigate the complexities of ZKML adoption, our analysis serves as a compass, guiding informed decision-making and catalyzing advancements in the realm of secure and transparent machine learning. Through collaboration, innovation, and continuous refinement, we aspire to foster a future where ZKML frameworks empower organizations to harness the full potential of machine learning while safeguarding data privacy and integrity.

V. Recommendations

In light of our benchmarking findings and analysis of EZKL, RISC Zero, and Orion ZKML frameworks, we offer a series of recommendations aimed at guiding stakeholders in their framework selection and deployment endeavors.

Summary of Benchmarking Findings

First and foremost, we summarize our benchmarking findings, emphasizing key performance differentials and nuances observed across EZKL, RISC Zero, and Orion. By distilling complex data into actionable insights, stakeholders gain a holistic understanding of each framework's strengths and limitations.

Key Factors Influencing Framework Performance

We elucidate the pivotal factors influencing framework performance, ranging from setup experiences and technical intricacies to implications for model accuracy. By contextualizing performance metrics within broader considerations, stakeholders can make informed decisions aligned with their specific needs and objectives.

Workflow Comparison and Recommendations

Drawing upon our analysis of setup experiences and technical aspects, we compare workflow efficiencies across EZKL, RISC Zero, and Orion. We highlight the advantages of integrated environments, such as EZKL's Jupyter notebook-based approach, which streamline the ML-to-ZKML pipeline and minimize cognitive load. Based on these insights, we recommend frameworks that prioritize user-friendly deployment procedures and seamless integration with existing ML workflows.

Areas for Further Optimization and Improvement

Recognizing the iterative nature of technological innovation, we identify areas ripe for further optimization and improvement within EZKL, RISC Zero, and Orion frameworks. From enhancing setup documentation to refining quantization strategies and memory management techniques, there exist ample opportunities to elevate the performance and usability of ZKML frameworks. By fostering collaboration and feedback loops, stakeholders can contribute to the continuous evolution of ZKML technologies.

Our recommendations serve as a roadmap for stakeholders navigating the complexities of ZKML framework selection and deployment. By leveraging our benchmarking findings and analysis insights, stakeholders can make informed decisions that align with their organizational objectives and foster innovation in the realm of secure and transparent machine learning. Through collaboration, knowledge-sharing, and a commitment to excellence, we envision a future where ZKML frameworks empower organizations to harness the full potential of machine learning while safeguarding data privacy and integrity.

Conclusion

In conclusion, our benchmarking study underscores the pivotal role of ZKML frameworks in ensuring the integrity and privacy of machine learning computations. Through rigorous experimentation and analysis, we have elucidated the performance differentials among EZKL, Orion, and RISC Zero, offering stakeholders invaluable insights into framework selection and optimization. As ZKML continues to evolve, our findings serve as a compass, guiding the trajectory of future advancements in secure and transparent machine learning.

About Orochi Network

Orochi Network is a cutting-edge zkOS (An operating system based on zero-knowledge proof) designed to tackle the challenges of computation limitation, data correctness, and data availability in the Web3 industry. With the well-rounded solutions for Web3 Applications, Orochi Network omits the current performance-related barriers and makes ways for more comprehensive dApps hence, becoming the backbone of Web3's infrastructure landscape.

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