Accelerators

AI Accelerators: GPUs, TPUs and Beyond AI workloads rely on hardware that can perform many operations in parallel. GPUs remain the most versatile starting point, offering strong speed and broad software support. TPUs push tensor math to high throughput in cloud settings. Beyond these, FPGAs, ASICs, and newer edge chips target specific tasks with higher efficiency. The best choice depends on the model size, the data stream, and where the model runs—on a data center, in the cloud, or on a device. ...

Deep Learning Accelerators: GPUs and TPUs Modern AI work often relies on specialized hardware to speed up work. GPUs and TPUs are the two big families of accelerators. They are built to handle large neural networks, but they do it in different ways. Choosing the right one can save time, money, and energy. GPUs at a glance They are flexible and work well with many models and frameworks. They have many cores and high memory bandwidth, which helps with large data and complex operations. They support mixed precision, using smaller numbers to run faster without losing accuracy in many tasks. Software is broad: CUDA and cuDNN on NVIDIA GPUs power popular stacks like PyTorch and TensorFlow. TPUs at a glance ...

Hardware-Software Co-Design for Performance Hardware-software co-design means building software and hardware in tandem to meet clear goals. It helps teams reach peak performance and better energy use. Start from the workload itself and the targets, not from a single component. By aligning on metrics early, you can spot bottlenecks and choose the right design split. Principles Start with workload and performance targets Gather data across layers: compiler, OS, and hardware counters Model trade-offs between speed, power, and silicon area Use clear abstractions to keep interfaces stable while exploring options Create fast feedback loops that show the impact of changes Optimize data movement and the memory hierarchy Real-world systems benefit when firmware, drivers, and the OS scheduler are part of the discussion. Data movement often dominates latency; moving computation closer to data can unlock big gains without sprawling hardware. ...

Building High-Performance Hardware for AI and Data Building high-performance AI hardware starts with a clear view of the workload. Are you training large models, running many inferences, or both? The answer guides choices for compute, memory, and data movement. Training favors many GPUs with fast interconnects; inference benefits from compact, energy-efficient accelerators and memory reuse. Start by mapping your pipeline: data loading, preprocessing, model execution, and result storage. Core components matter. Choose accelerators (GPUs, TPUs, or other AI chips) based on the workload, then pair them with fast CPUs for orchestration. Memory bandwidth is king: look for high-bandwidth memory (HBM) or wide memory channels, along with a sensible cache strategy. Interconnects like PCIe 5/6, NVLink, and CXL affect latency and scale. Storage should be fast and reliable (NVMe SSDs, tiered storage). Networking is essential for multi-node training and large data transfers (think 100G+ links). ...

Hardware Acceleration: GPUs, TPUs, and Beyond Hardware acceleration uses dedicated devices to run heavy tasks more efficiently than a plain CPU. GPUs excel at many simple operations in parallel, while TPUs focus on fast tensor math for neural networks. Other accelerators, such as FPGAs and ASICs, offer specialized strengths. Together, they speed up graphics, data processing, and AI workloads across clouds, desktops, and edge devices. Choosing the right tool means weighing what you need. GPUs are versatile and widely supported, with mature libraries for machine learning and high-performance computing. TPUs deliver strong tensor performance for large models in ideal cloud setups. Other accelerators can cut power use or speed narrow parts of a pipeline, but may require more development work. ...

Hardware Architecture for Efficient Computing Efficient computing starts with how data moves and how that flow fits the power budget. Modern systems mix CPUs, GPUs, and specialized accelerators. The goal is to do more work with less energy. Principles of energy-aware design Data locality matters: keep active data close to the processor, using caches effectively. Memory bandwidth is a bottleneck: design around reuse and streaming patterns. Heterogeneous compute helps: combine CPUs, GPUs, and accelerators for different tasks. Power management: use DVFS, clock gating, and sleep modes to save energy. Thermal design: heat limits performance; consistent cooling improves efficiency. Practical layouts for efficiency Balanced cores and accelerators: a mix of general cores and a few specialized units. Smart memory hierarchy: caches, memory controllers, and wide interconnects. Near-memory and compute-in-memory ideas: push some work closer to memory to reduce data movement. Efficient interconnects: scalable networks on chip and off-chip. A simple example Consider a 256x256 matrix multiply. If you tile the matrices into 64x64 blocks, each tile fits in a typical L2 cache. Each thread works on a tile, reusing A and B from cache to produce a tile of C. This reduces DRAM traffic and helps stay within power limits. For larger tasks, several tiles can be computed in parallel, keeping data hot in caches and registers. In practice, many systems use a small accelerator to handle common operations like matrix multiply, which cuts data movement and improves sustained throughput. Software must still map work to the right unit and keep memory access patterns predictable to sustain fast cache hits. ...

Hardware Accelerators: GPUs, TPUs, and Beyond Hardware accelerators unlock speed for AI, graphics, and data tasks. They come in several forms, from general GPUs to purpose-built chips. This guide explains how GPUs, TPUs, and other accelerators fit into modern systems, and how to choose the right one for your workload. GPUs are designed for parallel work. They hold thousands of small cores and offer high memory bandwidth. They shine in training large neural networks, running complex simulations, and accelerating data pipelines. In many setups, a CPU handles control while one or more GPUs do the heavy lifting. Software libraries and drivers help map tasks to the hardware, making it easier to use parallel compute without manual tuning. ...