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Guided learning journeys that build knowledge step by step.
category · Learning · slug · learning · 7813 Paths
7813 Paths · page 429 / 782
This advanced learning path equips environmentally conscious engineers with the knowledge to design computer systems that minimize energy consumption and electronic waste. Starting from foundational architecture concepts, it progresses through energy efficiency metrics, power management techniques, and sustainable design strategies, culminating in a holistic view of green computing.
This advanced learning path equips AI hardware designers with the knowledge to understand and design specialized architectures for AI and machine learning workloads. It covers the fundamentals of computer architecture and ML, then dives into dataflow architectures, systolic arrays, and specific implementations like NPUs and TPUs. The path emphasizes the rationale behind architectural choices and the trade-offs involved.
This path provides a structured journey through the unique architectural aspects of embedded systems, covering processor organization, memory constraints, I/O interfacing, and RTOS integration. It is designed for developers seeking to deepen their understanding of how embedded hardware and software interact.
This learning path equips engineers in mobile and embedded domains with advanced knowledge and skills to design energy-efficient computer architectures. It covers foundational concepts, key techniques like dynamic voltage/frequency scaling and power gating, and advanced topics including thermal management and energy-aware processor design.
This learning path guides VLSI and hardware designers through the complete ASIC design flow, from RTL design to GDSII, with a focus on architecture considerations that impact physical design. It covers synthesis, place and route, timing closure, and power analysis, emphasizing the interdependencies between architectural choices and physical implementation.
A comprehensive learning path covering GPU architecture fundamentals, the SIMT execution model, memory hierarchy, and practical programming with CUDA/OpenCL. Learners will progress from parallel computing basics to advanced optimization techniques, gaining the skills needed to write efficient GPU kernels.
This advanced learning path equips systems and cloud developers with a deep understanding of the hardware features that enable efficient virtualization on x86 platforms. It covers CPU virtualization (VT-x/AMD-V), memory virtualization (EPT/NPT), and I/O virtualization (SR-IOV), grounded in necessary OS and architecture concepts.
A learning path for system performance analysts to master techniques for optimizing memory system performance, covering cache behavior, prefetching, memory interleaving, bandwidth optimization, latency hiding, and NUMA considerations, with a foundation in performance analysis.
This advanced graduate-level path covers the principles and techniques for designing computer systems that continue operating correctly despite hardware faults. Learners will explore redundancy, error detection and correction, failover mechanisms, and N-modular redundancy, grounded in dependable computing concepts.
A comprehensive graduate-level path for researchers and designers to master the concepts and practices of modeling and simulating computer architectures, with a focus on performance modeling, trace-driven and execution-driven simulation, and the gem5 simulator.