Release Notes

Version 2.1.0 (2026-07-31)

General Notes

This release is heavily focused on robustness and production readiness, with dense compiler and CI improvements in addition to feature work.

release packages and new pip wheels

We don’t produce nor use the release tarball anymore (release.tar.gz); this tarball contained the binary elements of the release compiled for Ubuntu-24.04. Instead we recommended to use the various PIP wheel packages:

  • astra-sl-release: contains the binary runtime for the SL2610 board:

    • lib/syna_npu.ko: precompiled Linux kernel module for Torq NPU

    • tool/astra-sl-torq-run-module: precompiled userspace command for Torq runtime

  • astra-sl-runtime-wheel: PIP wheel package of Torq Runtime for SL2610 AstraMachina board.

  • host-compiler-wheel: PIP wheel package of Torq Compiler supporting x86-64 Ubuntu 24.04

  • host-runtime-wheel: PIP wheel package of Torq Runtime for Host based simulation (x86-64 Ubuntu 24.04)

  • host-turbine-wheel: new Python backend based on iree-turbine (supporting only Host based Ahead-Of-Time compilation)

About performance of compiled models

In terms of performance v2.1.0 may not as good as v2.0.0 for some models. While this is one of the major focus of this release the reason for this potential regression is the switch to generic linalg-slicing pass instead of homemade torq_hl-slicing (slicing is process of parallelisation between 2 slices - aka MAC engines - available in SL2610 SoCs). This new slicing pass is going to offer a nice boost in performance on the SL2610 AstrMachina or Google-Coral board but if you see any performance issue or regression this can be mitigated by disabling slicing using the compiler option –torq-disable-slicing.

Moreover, if you are using an official Astra image v2.5.x you will see a nice boost of performance thanks to the increased default frequency of the Torq NPU from 800MHz to 1GHz by default (this is governed by the newly introduced devfreq support).

Torq Compiler v2.1 changes since vs v2.0.0:

Highlights

  • Major Slicing improvements (parallelization across our 2 MAC engines - aka Slices).

  • Major expansion of quantized model support (Especially ONNX int8 quantized models).

  • Added int4 and int8 block quantization support, validated with Google Gemma3 LLM.

  • Added torq-turbine, a new PyTorch backend using forked iree-turbine.

  • Broad compiler correctness fixes across lowering and codegen paths.

  • Runtime improvements for x86 and Linux SL2610.

  • Significant CI, release pipeline, and test reliability upgrades.

Added

  • ONNX int8 quantization support.

  • int4/int8 block quantization support for LLM-oriented workflows.

  • New quantized lowerings (torch-onnx path), including Quantized MaxPool, GEMM, GlobalAveragePool and ElementWise Add.

  • torq-turbine test/backend path using forked iree-turbine.

  • Linux NPU devfreq support for SL2610.

  • CI support for versioned release tags and version-branch synchronization workflows.

Changed

  • Compiler pipeline behavior around elementwise coalescing and slicing.

  • QConv2D behavior to defer constant resolution to JIT in relevant flows.

  • EK-related behavior in HDIM and append/modulo paths.

  • Runtime Python behavior with x86 CModel inference enablement and improved version checks.

  • CI/release architecture toward cache/artifact-driven workflows, split Astra build jobs, and more wheel-based test execution.

Fixed

  • Grouped 1x1 conv miscompile via matmul-as-conv.

  • ConvTranspose fp32 bias-add lowering failures on NPU.

  • ReduceMean batch-dimension handling.

  • valid-to-same padding bug.

  • Rank-0 scalar broadcast in elementwise binary ops.

  • Clamp epilogue marking issue.

  • Gather lowering issue.

  • Depthwise stride-2 and asymmetric SAME-padding miscompile issues.

  • InstanceNormalization pure NSS lowering issue.

  • NDL cycle estimation issues.

  • Shared-config corruption in Keras layer tests.

  • Torchvision and test dependency robustness issues.

  • SoC build kernel-module configuration issue.

  • CI issues around release tests, docker build handling, and status report execution.

Full Changelog: https://github.com/synaptics-torq/torq-compiler/compare/v2.0.0…v2.1.0_beta

Version 2.0.0 (2026-06-20)

This is the second major release of the Torq Framework.

Important note about v1.5.x runtime compatibility

While we are confident that v2.0.0 compiler is way more capable and efficient than previous release, we understand that switching to a new major version may have unforeseen effect. To help with the transition we made sure that the v2.0.0 kernel driver can support both v1.5 and v2.0 runtime and compiled models.

Link with Astra-SDK build system

Astra-SDK for Synaptics SL2610 SoC family is automatically retrieving the source code from this github repo.

In the same way that Torq framework v1.5.x was released to support Astra-SDK v2.3.0, v2.0.x is supporting Astra-SDK v2.4.0

About performance

In terms of performance v2.0.0 is much better than v1.5.1 for most models, most notably YOLOv8 TFLite models are twice as fast. The performance gap is even bigger for LLMs and Transformers in general. As was demonstrated during the GoogleIO event Moonshine speech2text and Google-Gemma3 LLM are now fully accelerated on the NPU and deliver impressive performance on the SL2610 board. See our ready to use torq examples for those models and Google’s Coral board example page

Main changes in this release

The changes from v1.5.1 release are too numerous to list here, we will prepare extended release note as part of v2.0.0 but here are the main changes:

  • Updated to use IREE v3.10

    • Next version will upgrade to latest IREE

  • Vastly improved robustness, performance and model coverage

    • v2.0.0 has close to 6000 passing test cases (up from 1425 in v1.5.1).

  • torq-gen-config

    • This is new tool for the generation of working configurations for the compilation of ONNX models using heterogenous inference.

    • Working generated config are maintained in new git: torq-model-configs

  • Support for TOSA 1.0.1

  • Switch to use tosa-converter-for-tflite from ARM

    • Warning: Don’t use older method iree-import-tflite as this will produce uncompilable TOSA files.

  • Support for StableHLO input

  • LLM support: Google Gemma3 is our flagship model

  • Much improved Tile&Fuse and removed old torq-hl-tiling algorithm for tiling

    • As a consequence you cannot change the memory available for tiling using the –torq-hw option, this is now done automatically depending on the LRAM size.

  • Better Transformer model support

  • New or improved accelerated bf16 operations (sin, cos, sqrt, etc)

  • New generic linalg-slicing

    • This is not enabled by default as it needs to be optmized but you can try it by giving these options to torq-compile:

      torq-compile <…> –torq-disable-slicing=true –torq-disable-linalg-slicing=false

Version 1.5.1 (2026-04-20)

  • various fixes to Github CI

  • Update torq-runtime wheel name

  • Update model preload logic in python runtime helper script

  • Doc updates and runtime fixes

Version 1.5.0 (2026-04-03)

This is the second public release of the Torq Compiler.

v1.5.0 is using an old version of IREE from july 4th, 2024. We branched out from main in order to move forward with the upgrade of IREE which will take some time to stabilize. In the mean time branch v1.5 will be maintained with bug fixes and maybe some small features for at least a couple of month in order to support the usage of ASTRA SDK 2.3.

v1.5.0 offers stable and good performances on a number of models including:

  • MobilenetV2: int8 and bf16

  • YOLOv8s: Body Pose and ObjectDetection

  • YOLOv8n: ObjectDetection

  • Moonshine

  • SmolLM2

  • A big number of confidential customer models

WARNING v1.5.0 switched to the new generic algorithm for tiling that was formerly named as Super-Tiling and is now named Tile&Fuse or in short T&F. The deprecated torq-hl-tiling algorithm still offers better performances for models can still be enabled using the --torq-enable-torq-hl-tiling option.

For MobilenetV2 and YOLOv8 models we recommend to use --torq-enable-torq-hl-tiling for maximum performance; --torq-enable-transpose-optimization is also recommended for YOLOv8 performance.

Version 1.1.0 to 1.4.0

These correspond to Synaptics internal release were not formally tagged in the public git.

Version 1.0.0

v1.0.0 was the initial release as opensource in github, we never tagged nor created a binary package for it but it corresponds more or less to the ‘initial’ tag:

https://github.com/synaptics-torq/torq-compiler/releases/tag/initial

Version 0.9.0

Key Features & Enhancements

  • Initial release of the IREE-based MLIR compiler targeting Synaptics Torq.

  • Memory-aware tiling/slicing to fit on-chip memory.

  • Compile-time profiling for approximate clock-cycle estimates; runtime profiling for measured execution.

  • End-to-end validated on MobileNetV2.

  • Automatic CSS fallback for unsupported operators.

  • Supertiling (experimental): groups adjacent tiles into larger macro-tiles to improve locality and reduce launch/DMA overhead.

  • Support for compiling the model on custom Synaptics SoC hardware configurations.