# aws/aws-fpga

Official repository of the AWS EC2 FPGA Hardware and Software Development Kit

Repository: https://github.com/aws/aws-fpga
Canonical: https://ross.abutalabs.com/products/aws-fpga
Language: SystemVerilog
License: NOASSERTION
License Family: other
Last push: 2026-08-06T22:51:07+00:00

## Health v2 (maintenance only)
Score: 93/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 96, release rhythm 84, longevity 100
- inputs: {"age_days": 3589, "days_push": 27, "days_rel": 27, "gap_med": 33.5, "n_releases_24m": 17}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1677, forks 540 (observed 2026-08-28T04:05:20.926805+00:00)

## What it is
The official AWS EC2 FPGA Hardware and Software Development Kit for building hardware accelerators on EC2 F2 instances. It provides tools, interfaces, and documentation to architect, simulate, optimize, and deploy FPGA designs in the AWS cloud.

## Use cases
- develop fpga accelerators for aws ec2 f2 instances
- simulate and test fpga hardware designs in the cloud
- build custom hardware acceleration for compute workloads
- load and deploy bitstreams to aws fpga cards
- learn fpga development on aws with workshops and tutorials

## When to choose
- you are targeting AWS EC2 F2 instances for FPGA acceleration
- you need the official SDK and tooling for AWS FPGA hardware
- you want cloud-based FPGA development with AWS-supported documentation

## When to avoid
- you are developing for on-premises or non-AWS FPGA boards
- you need a general-purpose HDL simulator rather than an AWS-specific SDK
- your project does not involve FPGA hardware acceleration

## Facets
- artifact type: library
- maturity: active
- function: sdk, developer-tools, simulation
- domain: hardware, cloud-computing, developer-tools
- platform: cloud, cli
- tags: fpga, aws-ec2, f2-instances, systemverilog, hardware-acceleration, vivado, hdl, linux

## Member repositories
- aws/aws-fpga (main) score 93

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:20.926805+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:41:36.098390+00:00, confidence not recorded.
  - readme: https://github.com/aws/aws-fpga (fetched 2026-08-28T04:05:20.926805+00:00, sha cf1159c5a337)
- Data as of 2026-08-30T08:39:29.467469+00:00.