Comments (5)
Somehow the issue comes from re-writing the mult
array as a 1D array instead of a 2D array:
d90e74a#diff-b37c065f136460b015788b96b5c25102L96
Conversely if I comment out the mult
array partitioning (much like the weight
array is not partitioned), it also works again (although I see some other warnings)
https://github.com/hls-fpga-machine-learning/hls4ml/blob/d90e74a7c92eb63e675e27396177cb05b9286f00/nnet_utils/nnet_layer.h#L70
Maybe it would help if I knew why the weight
array partitioning was commented out. It looks like @benjaminkreis did it?
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Re weight array partition:
#17 (comment)
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just to record this for everyone...
So this error actually looks like a difference in Vivado version. This does not occur with 2017.2. It looks like Xilinx added an array size limit for fully partitioning in 2018.2 -- probably because of our complaints about RAM limits in the compiler 😱
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Error still exists with 16x100x100x100x100x5 example in hls4ml v0.6.0 and vivado 2020.1
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Hi @SteCla0, if you want to discuss, can you use the Discussions?
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Related Issues (20)
- Latency difference with same Model but version of hls4ml
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- ERROR: [XFORM 203-504] Stop unrolling loop 'Product1' (firmware/nnet_utils/nnet_dense_latency.h:37) in function 'nnet::dense_latency<ap_fixed<16, 6, (ap_q_mode)5, (ap_o_mode)3, 0>, ap_fixed<16, 6, (ap_q_mode)5, (ap_o_mode)3, 0>, config42_mult>' because it may cause large runtime and excessive memory usage due to increase in code size. Please avoid unrolling the loop or form sub-functions for code in the loop body. myproject_prj:solution1 Dec 27, 2023 6:47:26 PM
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