Fundamentals of Persistent Memory and Computational Memory Essentials Training by Tonex
Explore the transformative potential of persistent memory (PMEM) and computational memory in modern computing. This training dives into high-performance computing, AI/ML workloads, and in-memory computing trends. Attendees will gain actionable insights into memory technologies shaping the future. Designed for professionals seeking to optimize memory solutions, this program covers practical applications, integration techniques, and emerging innovations.
Audience:
Professionals in IT, AI/ML engineers, data scientists, software developers, and system architects.
Learning Objectives:
- Understand persistent memory and its applications.
- Explore computational memory for AI/ML.
- Learn in-memory computing advancements.
- Analyze memory trends in high-performance computing.
- Implement integration strategies effectively.
- Optimize workflows using memory technologies.
Course Modules:
Module 1: Introduction to Persistent Memory
- Overview of PMEM technologies.
- Key differences between PMEM and DRAM.
- Benefits in high-performance computing.
- Understanding NVDIMM standards.
- Persistent storage for cloud computing.
- Real-world PMEM use cases.
Module 2: Computational Memory Essentials
- Definition and concepts.
- Role in AI/ML workloads.
- Hardware requirements.
- Software stack optimization.
- Benefits for real-time analytics.
- Industry use case studies.
Module 3: In-Memory Computing Trends
- Emerging technologies overview.
- Accelerating database operations.
- Role in big data and analytics.
- Hybrid memory architecture.
- Limitations and challenges.
- Future outlook for in-memory systems.
Module 4: Integration Strategies
- Frameworks for PMEM deployment.
- Addressing compatibility issues.
- Transitioning legacy systems.
- Security considerations.
- Performance benchmarking.
- Cost-benefit analysis.
Module 5: AI/ML Workloads Optimization
- Leveraging computational memory.
- Data flow optimization techniques.
- Reducing latency in training models.
- Use in neural network inference.
- Case studies in AI innovation.
- Ethical considerations and impact.
Module 6: Practical Applications and Case Studies
- Real-world PMEM implementations.
- Computational memory for IoT.
- Addressing edge computing challenges.
- Case studies in financial tech.
- Hands-on session with tools.
- Review and wrap-up.
Transform your approach to memory technology. Enroll in this training today and lead the innovation in AI and high-performance computing.