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Add Concrete compiler to 'Users of MLIR' #196

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Mar 10, 2025
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Add Concrete compiler to 'Users of MLIR' #196

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Add Concrete compiler to 'Users of MLIR'
bcm-at-zama Jul 24, 2024
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joker-eph Mar 10, 2025
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23 changes: 22 additions & 1 deletion website/content/users/_index.md
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Expand Up @@ -46,9 +46,30 @@ constantly evolving, aiming to deliver execution on heterogenous architectures w
The CIRCT project is an (experimental!) effort looking to apply MLIR and the LLVM
development methodology to the domain of hardware design tools.

## [Concrete](https://github.com/zama-ai/concrete): TFHE Compiler that converts python programs into FHE equivalent

Concrete is an open-source framework that simplifies the use of
[Fully Homomorphic Encryption](https://fhe.org) (FHE) and makes writing FHE
programs easy for developers

FHE is a powerful technology that enables computations on encrypted data without
needing to decrypt it. This capability ensures user privacy and provides robust
protection against data breaches.

Concrete enables developers to efficiently develop privacy-preserving
applications for various use cases. For instance,
[Concrete ML](https://github.com/zama-ai/concrete-ml) is built on top of
Concrete to integrate privacy-preserving features of FHE into machine learning
use cases.

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Can you mdformat the file with 80 col wrapping?

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Absolutely! Done.

## [DSP-MLIR](https://github.com/MPSLab-ASU/DSP_MLIR): A Framework for Digital Signal Processing Applications in MLIR

DSP-MLIR is a framework designed specifically for DSP applications. It provides a DSL (Frontend), compiler, and rewrite patterns that detect DSP patterns and apply optimizations based on DSP theorems. The framework supports a wide range of DSP operations, including filters (FIR, IIR, filter response), transforms (DCT, FFT, IFFT), and other signal processing operations such as delay and gain, along with additional functionalities for application development.
DSP-MLIR is a framework designed specifically for DSP applications. It provides
a DSL (Frontend), compiler, and rewrite patterns that detect DSP patterns and
apply optimizations based on DSP theorems. The framework supports a wide range
of DSP operations, including filters (FIR, IIR, filter response), transforms
(DCT, FFT, IFFT), and other signal processing operations such as delay and gain,
along with additional functionalities for application development.

## [Enzyme](https://enzyme.mit.edu): General Automatic Differentiation of MLIR
Enzyme (specifically EnzymeMLIR) is a first-class automatic differentiation
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