Java Language & PlatformConference50min
High-Performance Java-Native AI with CUDA and the Vector API
This session shows how modern Java, using the Foreign Function & Memory API and Vector API, can run high-performance AI natively on CPUs and GPUs without JNI or Python. It includes Deep Netts CUDA integration, SIMD acceleration, and a fraud detection use case.
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Zoran SevaracDeep Netts
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Thursday, October 8, 10:40-11:30
TBA 6
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Historically, high-performance AI workloads have often depended on Python ecosystems, JNI-based bindings, or complex native integrations, leaving Java as a secondary choice. But the Java platform has changed dramatically. With the Foreign Function & Memory (FFM) API, the Vector API, and modern JVM capabilities, Java is becoming a first-class platform for AI development.
In this session, we'll show how the Foreign Function & Memory API and the Vector API enable a fully Java-native AI stack capable of running efficiently on both GPUs and CPUs. Using the latest release of Deep Netts, we'll demonstrate direct CUDA integration through the FFM API—without JNI or Python—and CPU acceleration using the Vector API for SIMD execution.
We'll present the architecture behind this approach, explain key implementation decisions, and compare it with traditional JNI- and Python-based solutions. The session also includes a real-world enterprise use case: AI-powered fraud detection, where low-latency inference and predictable performance are critical for banking and payment systems.
Whether you're interested in modern Java, high-performance computing, or enterprise AI, you'll leave with a practical understanding of how FFM, the Vector API, and GPU acceleration are transforming Java into a first-class platform for building and deploying production AI applications.
In this session, we'll show how the Foreign Function & Memory API and the Vector API enable a fully Java-native AI stack capable of running efficiently on both GPUs and CPUs. Using the latest release of Deep Netts, we'll demonstrate direct CUDA integration through the FFM API—without JNI or Python—and CPU acceleration using the Vector API for SIMD execution.
We'll present the architecture behind this approach, explain key implementation decisions, and compare it with traditional JNI- and Python-based solutions. The session also includes a real-world enterprise use case: AI-powered fraud detection, where low-latency inference and predictable performance are critical for banking and payment systems.
Whether you're interested in modern Java, high-performance computing, or enterprise AI, you'll leave with a practical understanding of how FFM, the Vector API, and GPU acceleration are transforming Java into a first-class platform for building and deploying production AI applications.
Zoran Sevarac
Zoran Sevarac is a seasoned software engineer, university professor of Artificial Intelligence, and passionate Java developer with more than two decades of experience in software engineering, AI research, and open-source innovation.
He is the creator of two pioneering AI development platforms built entirely in Java — Neuroph and Deep Netts — which made AI development more accessible to the Java community. His work bridges modern AI technologies with the reliability, performance, and scalability of the Java ecosystem.
A Duke’s Choice Award winner, Java Champion, and member of the JCP Executive Committee, Zoran has been instrumental in driving the evolution of AI support in Java. His mission is to simplify AI adoption, enable GPU acceleration through Project Panama and the Vector API, and bring high-performance, energy-efficient, and FinOps-friendly AI to enterprise systems.
He is the creator of two pioneering AI development platforms built entirely in Java — Neuroph and Deep Netts — which made AI development more accessible to the Java community. His work bridges modern AI technologies with the reliability, performance, and scalability of the Java ecosystem.
A Duke’s Choice Award winner, Java Champion, and member of the JCP Executive Committee, Zoran has been instrumental in driving the evolution of AI support in Java. His mission is to simplify AI adoption, enable GPU acceleration through Project Panama and the Vector API, and bring high-performance, energy-efficient, and FinOps-friendly AI to enterprise systems.