Beginners with basic programming knowledge who want to understand accelerated computing concepts, hardware architectures, and programming models.
- What is accelerated computing? Simple definition and core concepts
- Why traditional CPUs aren't enough for modern workloads
- The evolution from CPU-only computing to heterogeneous systems
- Types of accelerators: GPUs, FPGAs, ASICs, and specialized processors
- Real-world examples: How acceleration powers everyday technology (smartphones, gaming, AI assistants)
- Basic terminology and concepts that will appear throughout the series
- The performance-power efficiency tradeoff in computing
- How CPUs work: A simplified explanation of instruction execution
- The von Neumann architecture and its limitations
- Understanding clock speeds, cores, and threads
- CPU caches and memory hierarchy explained simply
- Introduction to instruction-level parallelism
- SIMD (Single Instruction Multiple Data) explained with examples
- How modern CPUs try to accelerate workloads
- When CPUs are the right tool for the job
- The origin story: From graphics rendering to general-purpose computing
- How GPUs differ from CPUs in architecture and design philosophy
- Understanding the massive parallelism of GPUs
- CUDA cores vs. Stream processors: NVIDIA and AMD terminology
- The GPU memory model for beginners
- Types of workloads that benefit from GPU acceleration
- Consumer vs. professional GPUs: What's the difference?
- Simple visualization of how data flows through a GPU
- What is CUDA and why it revolutionized GPU computing
- The CUDA programming model explained simply
- Understanding the host (CPU) and device (GPU) relationship
- Your first CUDA program: Hello World example with explanation
- Basic memory management: Host to device transfers
- Thinking in parallel: How to structure problems for GPU computation
- CUDA threads, blocks, and grids visualized
- Common beginner mistakes and how to avoid them
- Introduction to AMD's GPU architecture
- What is ROCm and how it compares to CUDA
- The HIP programming model: Writing portable GPU code
- Converting CUDA code to HIP: Basic principles
- Simple example of a ROCm/HIP program
- AMD's approach to open-source GPU computing
- When to choose AMD GPUs for compute workloads
- Resources for learning more about ROCm
- What are Tensor Cores and why they were developed
- Matrix multiplication: The foundation of deep learning
- How Tensor Cores accelerate matrix operations
- Mixed precision computing explained simply
- The impact on AI training and inference speed
- Comparing operations with and without Tensor Cores
- How to know if your workload can benefit from Tensor Cores
- Tensor Core generations and their evolution
- What is an NPU and how it differs from GPUs and CPUs
- The specialized architecture of neural accelerators
- Mobile NPUs: How your smartphone runs AI locally
- Edge computing and the role of NPUs
- Common NPU implementations in consumer devices
- Quantization and low-precision computing for efficiency
- Use cases: Image recognition, voice processing, and more
- The future of dedicated AI hardware
- Intel's journey into discrete graphics
- Understanding Intel's Xe architecture
- What is XeSS (Xe Super Sampling) and how it works
- Intel's oneAPI: A unified programming model
- Introduction to Intel's GPU computing capabilities
- AVX instructions: CPU-based acceleration explained
- Intel's vision for heterogeneous computing
- When to consider Intel's solutions for acceleration
- The graphics pipeline explained for beginners
- Rasterization vs. ray tracing: Different approaches to rendering
- Hardware-accelerated ray tracing: How it works
- Introduction to Vulkan: The modern graphics and compute API
- OpenGL: The classic graphics standard
- DirectX and Metal: Platform-specific graphics technologies
- Graphics vs. compute: Understanding the relationship
- How game engines leverage hardware acceleration
- What is SYCL and why it matters for portable code
- The challenge of writing code for multiple accelerators
- SYCL's programming model explained simply
- Comparison with CUDA and OpenCL
- Your first SYCL program with explanation
- How SYCL achieves performance portability
- The ecosystem around SYCL
- Real-world applications using SYCL
- Introduction to BLISS (Binary Large Instruction Set Semantics)
- The need for standardization in accelerated computing
- How BLISS aims to unify acceleration approaches
- The challenge of vendor-specific ecosystems
- Open standards vs. proprietary solutions
- The role of Khronos Group and other standards bodies
- How standards affect developers and users
- Future directions in acceleration standardization
- What is heterogeneous computing? Simple explanation
- Combining CPUs, GPUs, and other accelerators effectively
- The data movement challenge: Avoiding bottlenecks
- Task scheduling across different processor types
- Memory coherence explained simply
- Power management in heterogeneous systems
- Examples of heterogeneous systems in action
- Design considerations for mixed accelerator workloads
- Video encoding/decoding hardware explained
- Cryptographic accelerators and security processors
- Database and analytics acceleration techniques
- Scientific computing: Physics simulations and modeling
- Signal processing acceleration
- Image processing hardware
- Audio processing acceleration
- When to use specialized vs. general-purpose accelerators
- High-level frameworks: TensorFlow, PyTorch, and ONNX
- How frameworks abstract hardware details
- The tradeoff between ease of use and performance
- Domain-specific languages for acceleration
- Compiler technologies that enable acceleration
- Automatic optimization techniques
- Debugging and profiling accelerated code
- Choosing the right abstraction level for your project
- Setting up your development environment
- Choosing the right hardware for learning
- Cloud-based options for accessing accelerators
- Simple starter projects with source code
- Image processing acceleration project walkthrough
- Basic AI inference acceleration example
- Performance measurement and comparison
- Resources for further learning and practice
- Emerging hardware architectures to watch
- Photonic computing: Using light for computation
- Quantum acceleration: Basic concepts and potential
- Neuromorphic computing: Brain-inspired processors
- Specialized AI chips and their evolution
- The impact of accelerated computing on future applications
- Career opportunities in accelerated computing
- How to stay updated in this rapidly evolving field
- Key terminology definitions
- Visual diagrams explaining concepts
- Code snippets with line-by-line explanations
- "Try it yourself" exercises with solutions
- Common misconceptions addressed
- Real-world application examples
- Further reading resources for different learning levels
- Quick recap and preview of next lesson
Based on the current "Accelerating the Future" series, here are additional lessons that would complement and extend the curriculum:
- What are FPGAs and how they differ from GPUs and ASICs
- The architecture of FPGAs: LUTs, DSP blocks, and memory elements
- Hardware description languages: Introduction to VHDL and Verilog
- High-level synthesis: Programming FPGAs with C/C++
- FPGA development workflows and tools (Intel Quartus, Xilinx Vivado)
- Use cases: When FPGAs outperform other accelerators
- Real-world applications in networking, finance, and signal processing
- Getting started with affordable FPGA development boards
- What are ASICs and when to use them over other accelerators
- The ASIC design process: From concept to silicon
- Cost considerations: Development vs. production tradeoffs
- Famous examples of ASICs: Bitcoin miners, Google TPUs, Apple Neural Engine
- ASIC vs FPGA: Making the right choice for your application
- System-on-Chip (SoC) designs with integrated accelerators
- The future of application-specific hardware
- How startups are innovating with custom silicon
- The memory wall: Understanding bandwidth and latency challenges
- HBM (High Bandwidth Memory): How it powers modern accelerators
- GDDR vs HBM: Tradeoffs and applications
- Unified memory architectures explained
- Memory coherence protocols for heterogeneous systems
- Smart memory: Computational storage and near-memory processing
- Persistent memory technologies and their impact
- Optimizing memory access patterns for acceleration
- The importance of data movement in accelerated systems
- PCIe evolution and its impact on accelerator performance
- NVLink, Infinity Fabric, and other proprietary interconnects
- RDMA (Remote Direct Memory Access) technologies
- SmartNICs and DPUs (Data Processing Units)
- Network acceleration for AI and HPC workloads
- Distributed acceleration across multiple nodes
- Future interconnect technologies and standards
- Constraints and challenges of edge computing
- Low-power accelerators for IoT and embedded systems
- Mobile SoCs and their integrated accelerators
- Techniques for model optimization on edge devices
- Edge AI frameworks and deployment tools
- Real-time processing requirements and solutions
- Privacy and security considerations for edge acceleration
- Case studies: Smart cameras, autonomous drones, and wearables
- Quantum computing principles for classical programmers
- Quantum accelerators vs. full quantum computers
- Hybrid classical-quantum computing models
- Quantum algorithms that offer speedup over classical methods
- Current quantum hardware platforms and their capabilities
- Programming quantum systems: Introduction to Qiskit and Cirq
- Quantum machine learning: Potential and limitations
- Timeline and roadmap for practical quantum acceleration
- GPU-accelerated data processing with RAPIDS
- Database acceleration technologies (GPU, FPGA, custom ASICs)
- Accelerating ETL pipelines for big data
- In-memory analytics acceleration
- Graph analytics and network analysis acceleration
- Time series data processing optimization
- Visualization acceleration techniques
- Building an end-to-end accelerated data science workflow
- How compilers optimize code for accelerators
- Just-in-time (JIT) compilation for dynamic workloads
- LLVM and its role in heterogeneous computing
- Auto-vectorization and parallelization techniques
- Domain-specific compilers (XLA, TVM, Glow)
- Polyhedral optimization for accelerators
- Profile-guided optimization for hardware acceleration
- Writing compiler-friendly code for better performance
- Common challenges in debugging parallel code
- Tools for GPU debugging (CUDA-GDB, Nsight)
- Memory error detection in accelerated applications
- Performance profiling methodologies
- Identifying and resolving bottlenecks
- Visual profilers and timeline analysis
- Power and thermal profiling considerations
- Advanced debugging techniques for heterogeneous systems
- Overview of cloud-based accelerator offerings (AWS, GCP, Azure)
- Cost models and optimization strategies
- Serverless acceleration services
- Container-based deployment for accelerated workloads
- Managing accelerated clusters in the cloud
- Hybrid cloud-edge acceleration architectures
- Cloud-specific optimization techniques
- When to use cloud vs. on-premises accelerators
- Brain-inspired computing architectures
- Spiking Neural Networks (SNNs) explained
- Hardware implementations: Intel's Loihi, IBM's TrueNorth
- Programming models for neuromorphic systems
- Energy efficiency advantages over traditional architectures
- Event-based sensors and processing
- Applications in robotics, continuous learning, and anomaly detection
- The future of neuromorphic acceleration
- Physics-based simulation acceleration techniques
- Computational fluid dynamics (CFD) on accelerators
- Molecular dynamics and materials science acceleration
- Digital twin technology and hardware requirements
- Multi-physics simulation optimization
- Real-time simulation for interactive applications
- Visualization of simulation results
- Industry case studies: Automotive, aerospace, and manufacturing
- Power consumption challenges in accelerated computing
- Carbon footprint of training large AI models
- Sustainable practices in accelerator design and usage
- E-waste considerations for specialized hardware
- Democratizing access to acceleration technologies
- Bias and fairness in accelerated AI systems
- Responsible innovation in hardware acceleration
- Balancing performance with environmental impact
- Skills needed for different roles in accelerated computing
- Educational pathways and certifications
- Building a portfolio of accelerated computing projects
- Industry trends and job market analysis
- Specialization options: Hardware design, software development, research
- Contributing to open-source accelerated computing projects
- Networking and community resources
- Interview preparation and career advancement strategies
- Key terminology definitions
- Visual diagrams explaining concepts
- Code snippets with line-by-line explanations
- "Try it yourself" exercises with solutions
- Common misconceptions addressed
- Real-world application examples
- Further reading resources for different learning levels
- Quick recap and preview of next lesson
This series covers technologies and concepts that complement the existing "Accelerating the Future" and "Extended Accelerated Computing" series, focusing on emerging and specialized areas not thoroughly covered in those materials.
Intermediate to advanced learners who have completed the basic and extended accelerated computing series and want to explore cutting-edge technologies and specialized applications.
- Introduction to spatial computing paradigms
- Coarse-grained reconfigurable arrays (CGRAs)
- Dataflow architectures and their advantages
- Spatial vs. temporal computing models
- Programming models for spatial architectures
- Comparison with traditional von Neumann architectures
- Real-world implementations: Wave Computing, Cerebras, SambaNova
- Future directions in spatial computing design
- The fundamental concept of processing-in-memory (PIM)
- Near-memory vs. in-memory computing approaches
- Resistive RAM (ReRAM) and memristor-based computing
- Phase-change memory (PCM) for computational storage
- Analog in-memory computing for neural networks
- Digital in-memory computing architectures
- Addressing the memory wall through in-situ processing
- Commercial implementations and research prototypes
- Fundamentals of optical computing and photonic circuits
- Advantages of light-based computation: speed and energy efficiency
- Silicon photonics and integrated optical circuits
- Optical neural networks and matrix multiplication
- Coherent and non-coherent optical computing approaches
- Hybrid electronic-photonic systems
- Current limitations and engineering challenges
- Leading companies and research in optical acceleration
- Principles of probabilistic computing models
- Stochastic computing: representing data as probabilities
- Hardware implementations of probabilistic circuits
- Applications in machine learning and Bayesian inference
- Energy efficiency advantages for approximate computing
- Error tolerance and accuracy considerations
- Programming models for probabilistic accelerators
- Case studies in robotics, sensor networks, and AI
- Biological computation fundamentals
- DNA-based storage and processing
- Molecular algorithms and their implementation
- Parallelism in molecular computing systems
- Current capabilities and limitations
- Hybrid bio-electronic systems
- Applications in medicine, materials science, and cryptography
- Timeline for practical molecular accelerators
- Principles of superconductivity for computing
- Josephson junctions and SQUID-based logic
- Rapid Single Flux Quantum (RSFQ) technology
- Adiabatic quantum flux parametron (AQFP) logic
- Cryogenic memory technologies
- Energy efficiency and speed advantages
- Challenges: cooling requirements and integration
- Applications beyond quantum computing
- Revival of analog computing for specific workloads
- Continuous vs. discrete computation models
- Analog neural networks and their implementation
- Mixed-signal architectures combining digital and analog
- Current-mode and voltage-mode analog computing
- Noise, precision, and reliability challenges
- Programming and interfacing with analog systems
- Applications in edge AI and sensor processing
- Beyond binary: introduction to multi-valued logic
- Ternary computing architectures and advantages
- Hardware implementations of ternary logic
- Memory systems for multi-valued storage
- Energy efficiency and density benefits
- Programming models and compiler support
- Current research and commercial developments
- Applications in AI and post-Moore computing
- Thermodynamic limits of computing and Landauer's principle
- Reversible logic gates and circuits
- Adiabatic computing techniques
- Reversible instruction sets and architectures
- Quantum reversibility vs. classical reversibility
- Implementation technologies: CMOS, superconducting, optical
- Energy efficiency potential and practical limitations
- Programming models for reversible computation
- Trading accuracy for efficiency: the approximate computing paradigm
- Hardware support for approximate computing
- Precision scaling and dynamic accuracy management
- Approximate storage and memory systems
- Programming language support for approximation
- Quality metrics and error bounds
- Application domains: multimedia, sensing, machine learning
- Designing systems with controlled approximation
- Advanced neuromorphic circuit design principles
- Beyond Loihi and TrueNorth: emerging architectures
- Memristive devices for synaptic implementation
- Stochastic and probabilistic neuromorphic systems
- Learning algorithms for neuromorphic hardware
- Programming frameworks: Nengo, Brian, PyNN
- Sensory processing applications and event-based computing
- Neuromorphic robotics and embodied intelligence
- Coarse-grained reconfigurable arrays (CGRAs) in depth
- Runtime reconfigurable systems and partial reconfiguration
- Dynamically reconfigurable processor arrays
- Software-defined hardware approaches
- High-level synthesis advancements
- Domain-specific reconfigurable architectures
- Self-adaptive and self-optimizing hardware
- Future directions in reconfigurable computing
- HSA foundation and standards in depth
- Unified memory models for heterogeneous systems
- Queue-based task dispatching architectures
- System-level coherence protocols
- Power management in heterogeneous systems
- Runtime systems for dynamic workload balancing
- Compiler technologies for HSA
- Case studies of commercial HSA implementations
- CXL architecture and protocol details
- Memory semantics over PCIe infrastructure
- CXL.io, CXL.cache, and CXL.memory explained
- Coherent and non-coherent device attachment
- Memory pooling and disaggregation with CXL
- Comparison with other interconnects: CCIX, Gen-Z, OpenCAPI
- Implementation considerations and device support
- Future roadmap and impact on accelerated computing
- Specialized tensor architectures beyond Google's TPU
- Systolic arrays and their modern implementations
- Sparse tensor acceleration techniques
- Mixed-precision tensor computation
- Domain-specific tensor processors for different workloads
- Compiler optimizations for tensor operations
- Benchmarking and comparing tensor processors
- Emerging tensor processing paradigms
- Introduction to zero-knowledge proof systems
- Hardware acceleration for ZK-SNARKs and ZK-STARKs
- Blockchain and cryptocurrency acceleration
- Homomorphic encryption acceleration
- Post-quantum cryptography hardware
- Secure enclaves and trusted execution environments
- Privacy-preserving computation acceleration
- Applications in finance, identity, and secure computing
- Genomic sequencing and analysis acceleration
- Medical imaging processing architectures
- Real-time patient monitoring systems
- Drug discovery and molecular dynamics acceleration
- Brain-computer interfaces and neural signal processing
- Accelerating epidemiological models and simulations
- Privacy-preserving healthcare analytics
- Regulatory considerations for medical accelerators
- Perception pipeline acceleration for robotics
- SLAM (Simultaneous Localization and Mapping) hardware
- Motion planning and control system acceleration
- Multi-sensor fusion architectures
- Real-time decision making for autonomous systems
- Energy-efficient edge computing for mobile robots
- Hardware acceleration for reinforcement learning
- Case studies: drones, self-driving vehicles, industrial robots
- Monte Carlo simulation acceleration techniques
- Weather and climate modeling hardware
- Computational chemistry and materials science acceleration
- Finite element analysis optimization
- Lattice Boltzmann methods on specialized hardware
- Multi-physics simulation acceleration
- Visualization pipelines for scientific data
- Exascale computing architectures and applications
- Compute-centric vs. memory-centric architectures
- Processing-near-memory (PNM) architectures
- Smart memory controllers and intelligent memory
- Memory-driven computing models
- Non-volatile memory computing
- Storage class memory and computational storage
- Memory-centric programming models
- Future directions in memory-centric computing
- Side-channel attack vulnerabilities in accelerators
- Secure boot and attestation for specialized hardware
- Hardware isolation and sandboxing techniques
- Secure multi-tenant accelerator sharing
- Confidential computing on accelerators
- Hardware trojans and supply chain security
- Formal verification for accelerator security
- Regulatory and compliance considerations
- Specialized architectures for transformer models
- Attention mechanism hardware optimization
- Sparse and structured sparsity for NLP models
- Quantization techniques for language models
- Hardware for token generation and beam search
- Accelerating embedding operations
- Memory optimization for large language models
- Inference vs. training acceleration for NLP
- Graph representation and storage for acceleration
- Traversal-optimized architectures
- Specialized hardware for graph neural networks
- Memory access patterns for graph algorithms
- Partitioning and load balancing for distributed graphs
- Dynamic graph processing acceleration
- Applications: social networks, knowledge graphs, recommendation systems
- Benchmarking graph processing accelerators
- Hardware for policy evaluation and improvement
- Simulation acceleration for RL environments
- Specialized architectures for Q-learning and policy gradients
- Memory systems for experience replay
- On-device reinforcement learning acceleration
- Hardware-software co-design for RL algorithms
- Multi-agent RL system acceleration
- Applications in robotics, games, and control systems
- Energy-proportional computing design principles
- Advanced power management and harvesting techniques
- Carbon-aware computing and scheduling
- Recyclable and biodegradable computing materials
- Liquid cooling and heat reuse systems
- Ultra-low power accelerator designs
- Measuring and optimizing total carbon footprint
- Regulatory frameworks and green computing standards
- Specialized rendering architectures for AR/VR
- Foveated rendering acceleration
- Spatial computing hardware for mixed reality
- Hand and eye tracking acceleration
- Physics simulation for immersive environments
- Haptic feedback processing systems
- Low-latency wireless communication for AR/VR
- Power-efficient wearable acceleration
- Interfacing classical and quantum processors
- Quantum-accelerated machine learning
- Pre and post-processing for quantum algorithms
- Control systems for quantum hardware
- Quantum-inspired classical algorithms and hardware
- Variational quantum algorithms on hybrid systems
- Programming models for hybrid quantum-classical computing
- Near-term applications of quantum acceleration
- Ultra-low latency trading architectures
- Risk analysis and modeling acceleration
- Fraud detection hardware acceleration
- Blockchain and cryptocurrency mining optimization
- Options pricing and derivatives calculation hardware
- Regulatory compliance and reporting acceleration
- Market simulation and backtesting systems
- Secure multi-party computation for finance
- Signal processing architectures for 6G
- Massive MIMO and beamforming acceleration
- Machine learning for adaptive communications
- Network function virtualization hardware
- Software-defined radio acceleration
- Quantum communication interfaces
- Edge computing for distributed communications
- Security acceleration for next-gen networks
- Identifying transferable knowledge across accelerator types
- Developing hardware abstraction expertise
- Building skills in performance analysis and optimization
- Understanding energy efficiency tradeoffs
- Learning to evaluate new acceleration technologies
- Collaboration models between hardware and software teams
- Keeping up with research and industry developments
- Creating a personal learning roadmap for specialization
- Key terminology and concept definitions
- Architectural diagrams and visual explanations
- Comparative analysis with conventional approaches
- Current research highlights and breakthrough technologies
- Industry adoption status and commercial availability
- Programming considerations and software ecosystems
- Hands-on examples where possible with available technology
- Future outlook and research directions