KFR Roadmap

[dancazarin]

KFR 7 is scheduled for release this fall. It will be one of our most significant updates, introducing new features, broader platform support, and substantial performance improvements.

Below is an overview of the planned features across upcoming releases:

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New SIMD-optimized functions

Expanded set of low-level primitives for bit manipulation, 3D graphics, and advanced math operations, all optimized with SIMD instructions to maximize throughput on modern CPUs.

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New audio I/O implementation

Support for reading WAV, AIFF, CAFF, FLAC, and MP3 formats, and writing WAV and AIFF.
Includes highly optimized conversions to specific formats.

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DFT refactoring

A complete rework of the Discrete Fourier Transform implementation for greater flexibility, modularity, and raw performance, enabling more efficient integration in signal processing workflows.

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C++20 integration

Adoption of modern C++20 features to simplify expression handling, function overloading, and template metaprogramming, allowing the codebase to focus on clarity, maintainability, and cutting-edge optimizations.

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Elliptic filters

Introduction of elliptic IIR filters, expanding the library’s DSP toolbox with more flexible filter design options, enabling sharper roll-off and higher selectivity in practical applications.

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Zero-Phase IIR Filter

Implementation of zero-phase filtering using forward-backward IIR filtering techniques, allowing users to apply IIR filters without phase distortion.

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Fat binary support for macOS

Support for universal binaries on macOS, enabling seamless execution on both Intel and Apple Silicon (M-series) architectures without the need for separate builds.

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Better GCC and MSVC support

While Clang remains ahead in vector optimization, KFR will provide improved support for GCC and MSVC, helping these compilers generate more efficient SIMD instructions and narrowing the performance gap.

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Half precision support on ARMv8.2

Native float16 support in vec<> types on ARMv8.2+ CPUs, allowing higher throughput and reduced memory bandwidth in machine learning and DSP workloads.

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Better documentation

A redesigned documentation system with:

• Clearer examples and use cases
• API reference improvements
• Guides for performance tuning and integration
  This will make onboarding smoother and expert usage more efficient.

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New architecture: RISC-V

Initial support for RISC-V vector extensions, enabling KFR to run efficiently on the rapidly growing RISC-V ecosystem and broadening its role in next-generation embedded and HPC systems.

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Real-world examples

A collection of ready-to-use, production-grade examples demonstrating KFR in domains such as:

• Audio signal processing
• Spectral analysis
• Embedded DSP applications
• Machine learning preprocessing
  These examples aim to reduce integration time and showcase practical best practices.