Why Memory Bandwidth Often Limits AI Chips More Than Compute
Understand the memory wall in AI chip architecture, how to tell if a workload is compute-bound or memory-bound, and what designers do about it.
AI Semiconductors & Chips
The silicon behind modern AI.
AI Semiconductor Technology, AI Chip Architecture, GPU & AI Accelerators, Semiconductor Manufacturing and Edge AI Hardware explained.
Latest guides
Browse categories Numeric Precision in AI Hardware: FP32, BF16, FP8 and INT8 Explained
What FP32, BF16, FP16, FP8 and INT8 mean for AI hardware, why lower precision runs faster, and how to pick a precision without hurting accuracy.
On-Chip Memory in AI Accelerators: SRAM, Caches and Scratchpads
How on-chip SRAM, caches and software-managed scratchpads shape AI accelerator performance, and what tiling means for the way you run a model.
Systolic Arrays, SIMD and Dataflow: Comparing AI Compute Designs
A side-by-side look at the main compute styles inside AI chips, what each is good at, and how they shape programming and flexibility.
How AI Chips Differ From General-Purpose CPUs
A plain explanation of why AI workloads run on specialised silicon, and how to decide when a CPU is still the right tool for the job.
GPU, FPGA or ASIC: Matching AI Silicon to the Workload
How to decide between GPUs, FPGAs and custom ASICs for an AI workload, using flexibility, volume, latency and software effort as the deciding factors.