Pinned Repositories
asraa.github.io
build
Build-related tools for TensorFlow
byob-test
circt-hls
CIRCT-based HLS compilation flows, debugging, and cosimulation tools.
clusterfuzz
Scalable fuzzing infrastructure.
cocoabots
cocoabots maslab 2015
codeql-uboot
slsa-example
slsa-on-github
Non-falsifiable SLSA provenance on GitHub workflows
asraa's Repositories
asraa/slsa-on-github
Non-falsifiable SLSA provenance on GitHub workflows
asraa/byob-test
asraa/circt-hls
CIRCT-based HLS compilation flows, debugging, and cosimulation tools.
asraa/cosign
Container Signing
asraa/example-package
asraa/fulcio
SigStore WebPKI
asraa/fully-homomorphic-encryption
An FHE compiler for C++
asraa/go-tuf
Go implementation of The Update Framework (TUF)
asraa/heir
Standard MLIR dialects for homomorphic encryption
asraa/heir-play
An ipython/jupyter magic for running HEIR commands
asraa/hello-transparent-release
asraa/in-toto-golang
A Go implementation of in-toto. in-toto is a framework to protect software supply chain integrity.
asraa/llvm-project
The LLVM Project is a collection of modular and reusable compiler and toolchain technologies. Note: the repository does not accept github pull requests at this moment. Please submit your patches at http://reviews.llvm.org.
asraa/mlir-pass
Standalone MLIR pass to lower global memref ops to affine
asraa/mlir-python-extras
The missing pieces (as far as boilerplate reduction goes) of the upstream MLIR python bindings.
asraa/my-action
An example GitHub Action
asraa/my-action-test
asraa/rekor
Signature Transparency Log
asraa/retvec
Experiments with retvec
asraa/root-signing
asraa/scorecard
Security Scorecards - Security health metrics for Open Source
asraa/slsa
Supply-chain Levels for Software Artifacts
asraa/slsa-byob-template
Create a SLSA builder reusable workflow using your tool.
asraa/slsa-github-generator
asraa/slsa-on-github-test
Test for SLSA
asraa/slsa-verifier
asraa/starter_mlproject
This is a ML project
asraa/test
This is a test project
asraa/test-reusable-workflow
asraa/tflite-micro
Infrastructure to enable deployment of ML models to low-power resource-constrained embedded targets (including microcontrollers and digital signal processors).