9. Release Notes¶
9.1. TIDL_PSDK_8.6¶
New devices
Support AM62A - To compile model for AM62A, set
platform
argument to “AM62A”, see Compiling Models for details.
Bug fixes in C7x code generation
Fix DMAPass src on/off chip check
Fix scatter_nd code generation
9.2. TIDL_PSDK_8.5¶
Bug fixes in C7x code generation
Support non-contiguous access pattern in DMA
Support strided access pattern in SE
Support odd number of DMA blocks and iterations
Simplify resize2d index computation
Optimize maxpool2d with 1x1 pool size
TIDL offload
Rewrite broadcast 1D “add” as “bias_add” and offload to TIDL
Do not offload resize2d with asymmetric or non-power-of-2 scaling factor
Enabled TIDL batch processing
Rewrite conv2d with 1x2 strides as conv2d with 1x1 strides followed by maxpool2d with 1x2 strides
Offload sigmoid to TIDL
TVM extension
Support calling external function with DLTensor args
Demonstrate overwriting default TVM strategy for an operator
Demonstrate overwriting default TVM strategy for an operator for a special case
9.3. TIDL_PSDK_8.4¶
Bug fixes in C7x code generation
Fix streaming engine pass for loops containing multiple accesses to same tensor
Add nop() function to support Reshape op in TVM C runtime
Avoid overriding generic op strategy in “hls.py” (back-ported from upstream TVM)
Add C7x strategy for concatenate instead of using generic strategy
Do not vectorize inner loop with iterations less than vector length
Do not vectorize if loop body contains call
Fix vectorization factor to use largest data type in computation
Add 64-bit integer support in streaming engine config
Fix TVM C runtime to support more than 255 functions/layers
Return tvm runtime create failure to OpenVX node
Apply tiling and dma schedule only to broadcast ops in injective.py
J721S2 support
Merge with upstream neo-ai tvm 1.11.2
Tensor debug support for TVM Arm runtime and TVM C7x runtime
9.4. TIDL_PSDK_8.2¶
Initial release of C7x code generation support
Merge with neo-ai-tvm 1.10.0