Jacinto 7 TIDL Release Notes
Version: 08.04.00, Date: Aug 23, 2022
Contents
- Introduction
- Licensing
- What's New
- Documentation
- Upgrade and Compatibility Information
- Device Support
- Validation Information
- Fixed Issues
- Known Issues
- Change Request
- Technical Support
- Package Versioning
Introduction
This package contains Texas Instrument's Deep learning solution for J721E SoCs from the Jacinto 7 family
Licensing
The licensing information of this product, as well as any third-party components included which are made available under a number of other open-source licenses are enumerated as part of the manifest. Note : Below Links would work only after installing the package
Documentation
1 | User's Guide | [HTML] |
2 | Software Manifest | [HTML] |
What's New
Here are a few of the new features supported in this release:
- Grouped convolution performance improvements
- OSRT RTs (TFlite, ONNX runtime) validated with Ubuntu 18.04 and Ubuntu 20.04 Docker containers on EVM
Upgrade and Compatibility Information
This release is interface compatible with previous release. Note : Model's imported/compiled with previous release versions will not work with this release. User is expected to re-import/re-compile models with this release before using it
Device Support
Target SoC | Target Plaform | Build environment (OS) | Target environment (OS) |
J721E EVM | x86_64 Linux PC | x86_64 (Linux) | NA |
Validation Information
This release was built and validated using the following tools:
Dependencies (included in Processor SDK RTOS):
- ivision - 01_00
- MMALIB - 02_04_00_05
- PDK - CORE SDK 08.04.00
- C7x CGT - ti-cgt-c7000_3.0.0.STS
Dependencies (NOT included in Processor SDK RTOS):
- GCC version 5.4.0 (In Ubuntu 18.04)
- OpenCV: 4.1.0 (for Test bench Only)
- Protobuf: 3.11.3 (for Import tool only)
- Flatbuffers : 1.12.0 (for Import tool only)
Fixed Issues
ID | Description | Module | Affected Versions | Affected Platforms |
---|---|---|---|---|
[TIDL-605] | Spatial pooling + inner-product will not generate error | TOOL | 08.02.00, 08.01.00, 08.00.01, 08.00.00, 02.00.00, 01.04.00, 01.03.00, 01.02.00, 01.01.00, 01.00.00 | J721E |
[TIDL-708] | Network compiler should detect invalid layers that have inW=inH=outW=outH=No=Ni=0 and return an error | TOOL | 08.02.00, 08.01.00,08.00.01, 08.00.00, 02.00.00, 01.04.00, 01.03.00, 01.02.00, 01.01.00 | J721E |
[TIDL-864] | DNN with Eltwise or Concat layers may have functional issue under some remote cases | COMPUTE | 08.02.00, 08.01.00,08.00.01, 08.00.00, 02.00.00, 01.04.00, 01.03.00, 01.02.00 | J721E |
[TIDL-1350] | Accuracy of ONNX RT is poor than TVM (NEO AI DLR) for same model for some models | COMPUTE | 08.02.00, 08.01.00,08.00.01, 08.00.00, 02.00.00 | J721E |
[TIDL-1816] | Model cannot be executed if number of input dimensions of the network/network subgraphs is not equal to 4 | TOOL | 08.02.00, 08.01.00,08.00.01 | J721E |
[TIDL-1874] | Documentation update : Clearly indicate that post processing output dumped during import pass is not representative of inference output | Documentation | 08.02.00, 08.01.00 | J721E |
[TIDL-1893] | Auto pad attribute "SAME LOWER" (for convolution and pooling) not imported correctly as part of ONNX import | TOOL | 08.02.00, 08.01.00 | J721E |
[TIDL-1915] | Convolution layer with pad = 0 may result in a hang/crash | COMPUTE | 08.02.00, 08.01.00 | J721E |
[TIDL-2360] | SSD Network with more than 8 Priorbox is not working | COMPUTE | 08.02.00, 08.01.00, 08.00.01 | J721E |
[TIDL-2385] | ONNX Split layer not getting imported correctly | TOOL | 08.02.00 | J721E |
[TIDL-2391] | ONNX import : Pooling layer with stride = 2 behaves incorrectly when padL < padR or padT < padB | TOOL | 08.02.00 | J721E |
[TIDL-2520] | Global average pooling misbehaves with 16 bit and output feature map placed in DDR | COMPUTE | 08.02.00 | J721E |
[TIDL-2528] | Convolution (non fully grouped case) layer may have functional issue with 16 bit during some specific cases | COMPUTE | 08.02.00, 08.01.00,08.00.01, 08.00.00 | J721E |
[TIDL-2529] | Clip activation merged with EltWise layer does not work as expected | COMPUTE | 08.02.00 | J721E |
[TIDL-2556] | Custom Layer : DMA abstraction API's datatype is different from documentation | COMPUTE | 08.02.00 | J721E |
[TIDL-2559] | Global Average pooling with 1x1 spatial input resolution gives incorrect output on EVM | COMPUTE | 08.02.00 | J721E |
Known Issues
ID | Description | Module | Reported in Release | Affected Platforms | Occurrence | Workaround in this release |
---|---|---|---|---|---|---|
TIDL-1197 | Model execution order for a branch is not optimal | COMPUTE | 01.02.00 | J721E | Rare | No fucntional issue, Only Performance drop would be obsrved |
TIDL-1530 | Processing on EVM gets stuck in case of "GEMM" layer in onnx model | OSRT | 02.00.00 | J721E | Rare | set reservedCtrl=1 of TIDL_CreateParams |
TIDL-1872 | Import failure for deeplabv3 (mobilenetv2 backbone) based OD+SEG Model | COMPUTE | 08.01.00 | J721E | Rare | |
TIDL-2593 | OSRT : Softmax layer with non-flattened input gets delegated to C7X-MMA instead of ARM | OSRT | 08.02.00 | J721E | Rare | Introduce a flatten layer before softmax or set softmax layer in deny_list runtime option |
For latest status of the issues and any new issues found post this release, please refer TI External software Incident Report. Issues on this product can be filtered with search query as project = EXT_EP AND issuetype in (Bug, Enhancement) AND Product ~ "TI Deep Learning Library"
Change Request
ID | Description | Original Fix Version | New Fix Version |
---|
Technical Support
For technical support, please post your questions on TI E2E Forum for Processors.
For additional assistance, contact local TI Field Application Engineer
Package Versioning
Each package version is composed of 4 period-delimited numbers - represented here by the letters M, m, p and b [M.m.p.b]
. The table below provides a descriptive reference regarding package version numbering.
Digit | Meaning | Description |
---|---|---|
1 (M=Major) | Major revision | Incremented when the new version is substantially different from the previous For example, a new module added or an existing module's algorithm significantly altered. |
2 (m=minor) | Minor revision | Incremented when the new version has changed but not in a major way. For example, some minor changes in the API or feature set. |
3 (p=patch) | Patch number | Incremented for all other source code changes. This include any packaging support code. |
4 (b=build) | Build number | Incremented for each release delivery to CM. Reset for any change to M, m or p |
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