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HiFi_iQ_NDSP

NatureDSP Library for HiFi iQ DSP cores Note: This is the NDSP early access release for HiFi iQ DSP using RJ-2026.6 toolchain.

The repo is organized as follows.

xws:

  • This release of the NDSP contains two xws files.

  • An xws each, for the library-kernels and the test-driver. Ex : HiFi_iQ_VFPU_library_v0_8_0.xws & HiFi_iQ_VFPU_demo_v0_8_0.xws

  • Building and executing the xws in Xtensa Xplorer is described in the API Reference Document.

  • Detailed release documentation can be extracted from lib.xws/doc folder.

Release v0.8.1 Brief:

  • This patch update extends support to DSP configurations with the Recip/RSQRT QLI option disabled, ensuring clean compilation and correct execution on such cores.

Release v0.8.0 Brief:

  • Release Date : Jun-2026.
  • This release is targeted for xt-clang compiler
  • All testing and performance measurements have been done on xt-clang using RJ-2026.6 version of the Xtensa Xplorer and tools.
  • Four different core configurations are used to build and verify this package hifiiq_ao_so7_BM: Speed Optimized core with All options enabled hifiiq_ao_co7_BM: Cycle Optimized core with All options enabled hifiiqBase_so7_BM: Speed Optimized Base Core (doesnot contain Half Precision, Single Precision or Double precision floating point unit and AI Unit) hifiiq_dspai_so7_BM: Speed Optmized core without double precision floating point unit.
  • The library is tested with sanity test vectors shared along with this package
  • Although many APIs are described in the reference document/User Guide, only certain APIs are available in this release. Remaining APIs will be made available in the subsequent releases.
  • In this release there is limited support for fixed point FFTs / IFFTs. Only powers of 2 sizes are supported. Non powers of 2 sizes and improved memory efficiency (_ie) variants will be supported in subsequent releases

NDSP_HiFi_iQ

This contains the source code along with make files that will build in linux environment.

doc folder

This contains help documentation on how to build the source code in linux and run the performance and functional regressions.

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