{
 "id": "1.08",
 "slug": "fpgas-inference-networking",
 "url": "/parts/compute-silicon-and-packaging/fpgas-inference-networking",
 "name": "FPGAs",
 "qualifiers": "inference, networking, test",
 "layer": {
  "id": 1,
  "name": "Compute silicon & packaging"
 },
 "parent_id": null,
 "children": [],
 "summary": "Field-programmable chips used in AI clusters for inference offload, networking functions, and hardware test and bring-up, rather than as the primary training accelerator.",
 "description_md": "FPGAs are reconfigurable logic chips that pick up work a fixed-function accelerator design doesn't fit well: certain inference workloads, custom networking functions that sit near the NIC and DPU layer (NICs / SuperNICs, DPUs), and test and bring-up tasks used across the semiconductor supply chain (Semiconductor test & burn-in) as new AI silicon comes online. They're a smaller, more specialized category than the GPU and ASIC accelerators that dominate the layer, and one where volume is driven by breadth of use case rather than by cluster-wide compute scaling.\nInvestor exposure is concentrated in a small number of vendors, and the category's growth is tied less to overall AI buildout volume than to how many distinct niches — inference offload, networking, test — continue to favor reconfigurable logic over a purpose-built ASIC. That makes it a steadier, lower-beta way to have AI exposure within compute silicon than the accelerator vendors themselves, at the cost of a smaller addressable opportunity.",
 "ai_delta": "FPGAs pick up AI inference and networking tasks where a fixed accelerator design doesn't fit, and see added demand from the test and bring-up work that a growing volume of new AI silicon generates.",
 "bottleneck_status": null,
 "scale_tiers": [
  "enterprise",
  "hyperscale_building",
  "ai_campus",
  "gw_class"
 ],
 "density_modes": [
  "any"
 ],
 "scale_notes": "Deployed in smaller, targeted volumes tied to specific use cases rather than scaling linearly with accelerator count as facility size increases.",
 "flow": [
  "compute"
 ],
 "companies": [
  {
   "company_id": "amd",
   "name": "AMD",
   "role": "manufacturer",
   "confidence": "inferred",
   "ownership_type": "public",
   "ticker": "AMD",
   "product_note": null,
   "note": null,
   "source_url": null
  },
  {
   "company_id": "lattice",
   "name": "Lattice",
   "role": "manufacturer",
   "confidence": "inferred",
   "ownership_type": "public",
   "ticker": "LSCC",
   "product_note": null,
   "note": null,
   "source_url": null
  },
  {
   "company_id": "achronix",
   "name": "Achronix",
   "role": "manufacturer",
   "confidence": "inferred",
   "ownership_type": "private",
   "ticker": null,
   "product_note": null,
   "note": null,
   "source_url": null
  },
  {
   "company_id": "altera",
   "name": "Altera",
   "role": "manufacturer",
   "confidence": "inferred",
   "ownership_type": "unknown",
   "ticker": null,
   "product_note": null,
   "note": "verify",
   "source_url": null
  }
 ]
}