Certified AI Accelerator Programmer (C-AIAP) Certification Program by Tonex
Certified AI Accelerator Programmer (C-AIAP) prepares you to build high-performance GPU applications that meet strict throughput and latency SLOs. The program focuses on CUDA, ROCm/HIP, and SYCL so you can design portable kernels and tune them for data center and edge deployments. You will master parallel execution, memory hierarchies, synchronization, and numerics, then translate those concepts into efficient kernels, reliable pipelines, and predictable service behavior.
Performance must be secure and dependable. The course shows how to engineer for p95/p99 latency, control jitter, and contain tail amplification. It also addresses cybersecurity for accelerators: constant-time patterns, preventing data leakage across shared resources, mitigating side-channels, and maintaining supply-chain integrity in toolchains. You will learn to use profiling and observability to explain bottlenecks, validate SLOs, and sustain performance under change.
Graduates leave with a blueprint for portable performance: write once, tune everywhere, and operate with confidence. Outcomes include faster models, lower cost per inference, and safer deployments in zero-trust environments.
Learning Objectives:
- Implement GPU kernels using CUDA, ROCm/HIP, and SYCL.
- Optimize memory traffic with tiling, coalescing, and shared memory.
- Balance occupancy, registers, and instruction throughput.
- Engineer pipelines to satisfy throughput and latency SLOs.
- Profile and trace GPU applications to remove bottlenecks.
- Reduce p95/p99 tail latency and jitter in production.
- Apply constant-time and memory-safe coding patterns.
- Build portable toolchains and maintainable codebases.
Audience:
- Cybersecurity Professionals
- GPU/AI/ML Engineers
- Performance Engineers and SREs
- Systems and Cloud Architects
- Data Scientists and MLOps Engineers
- DevOps and Platform Engineers
- Edge/Embedded AI Developers
- Technical Team Leads
Program Modules:
Module 1: GPU Architecture & Parallelism Foundations
- Streaming multiprocessors / compute units and execution model
- Threads, warps/wavefronts, grids, and work-group mapping
- Memory hierarchy: registers, shared/LDS, L2, global
- Synchronization primitives and barriers
- Control-flow divergence and predication
- Occupancy, ILP, and latency hiding
Module 2: CUDA, ROCm/HIP, and SYCL Essentials
- Kernel structure, launches, and execution configuration
- Device/host memory management and transfers
- Streams, queues, and asynchronous execution
- SYCL buffers vs USM and command groups
- HIP portability patterns and API interop
- Build systems, compilation flags, and tooling
Module 3: Kernel Design, Fusion & Optimization
- Tiling strategies and shared-memory staging
- Memory coalescing and avoiding bank conflicts
- Vectorization and use of tensor/matrix cores
- Precision choice, numerics, and stability
- Register pressure tuning and spills mitigation
- Kernel fusion vs modularity trade-offs
Module 4: Throughput & Latency SLO Engineering
- Defining SLOs, SLIs, and error budgets
- Batching, micro-batching, and back-pressure control
- Real-time scheduling and queueing basics
- Warmup, caching, and startup transients
- p95/p99 analysis and tail control techniques
- Autoscaling, placement, and resource quotas
Module 5: Profiling, Debugging & Observability
- Timeline, kernel, and memory profiling workflows
- Hardware counters, roofline, and bottleneck analysis
- Tracing across CPU–GPU boundaries
- Debugging race conditions and nondeterminism
- Continuous performance regression testing
- Metrics, alerts, and performance runbooks
Module 6: Secure & Reliable Accelerator Programming
- Constant-time patterns and secret-safe control flow
- Memory safety, bounds checks, and sanitizer use
- Multi-tenant isolation and data remanence controls
- Side-channel awareness and noise mitigation
- Dependency hygiene and signed artifacts
- Reproducible builds and rollout strategies
Exam Domains:
- Accelerator Systems Theory and Design
- Portable GPU Programming Paradigms
- Kernel Performance Engineering and Tuning
- SLO-Driven Runtime and Operations
- Observability, Diagnostics, and Reliability
- Secure Acceleration and Supply-Chain Integrity
Course Delivery:
The course is delivered through lectures, interactive discussions, guided demonstrations, and case-study walkthroughs led by experts in the field of Certified AI Accelerator Programmer (C-AIAP). Participants receive curated online resources, including readings, design templates, checklists, and reference implementations for structured practice.
Assessment and Certification:
Participants are assessed through quizzes, assignments, and a capstone project. Upon successful completion of the course, participants will receive a certificate in Certified AI Accelerator Programmer (C-AIAP).
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the Certified AI Accelerator Programmer (C-AIAP) Certification Training exam, candidates must achieve a score of 70% or higher.
Accelerate your AI with confidence. Enroll in C-AIAP to master CUDA/ROCm/SYCL, hit your SLOs, and harden your deployments. Contact Tonex to schedule a cohort or bring this program to your team.