Moore’s Law no longer buys free performance. The industry’s answer has been specialisation: a growing menagerie of silicon brains, each optimised for a different shape of work. This essay is a field guide to that landscape — 19 processor types, from production hardware you can rent today to research architectures that may redefine the next decade.
I organise them with Technology Readiness Levels (TRL), borrowed from aerospace: production-ready (TRL 7–9) versus emerging (TRL 2–7). The goal is not encyclopaedic trivia. It is to know which chip removes a real constraint for your workload — training, inference, edge, networking, security — and which is still a bet.
What the full essay covers
Production arsenal. ASICs for fixed high-volume pipelines; GPUs and the CUDA moat; Google’s TPU systolic arrays; on-device NPUs; DSPs for deterministic signal paths; Graphcore’s IPU; DPUs that strip infrastructure tax from CPUs; CGRAs as the flexible middle ground; ubiquitous MCUs and TinyML; vision-focused VPUs; and the quieter niches of SPUs and PPUs.
The frontier. 3D chiplets and CoWoS packaging; Cerebras wafer-scale engines; processing-in-memory; photonic compute; neuromorphic spiking silicon; quantum processing units; analog AI; and DNA/molecular storage and computation.
Each section pairs architecture with economics, software ecosystem, and the honest weakness — the place where the silicon stops helping.
Read the original on Medium
This page is the abstract. The full technical map — depth, references, and when to choose what — is on Medium:
The Processor Menagerie: Every Computing Brain Powering the AI Era — Explained
Filed under
- AI hardware
- Processors
- GPUs
- Edge AI
- Semiconductors