![]() dtype ) # Apply packing to the A and B arrays from a 2D to a 4D packed layout A_packed = A_orig. ext_dev ( 0 ) # Initialize the A and B arrays randomly in the int range of (-128, 128] A_orig = np. ![]() # Get the remote device context ctx = remote. This concludes the scheduling portion of this tutorial. With T.attr(T.iter_var(vta, None, "ThreadIndex", "vta"), "coproc_scope", 3): T.attr(T.iter_var(vta, None, "ThreadIndex", "vta"), "coproc_scope", 2) With T.attr(T.iter_var(vta, None, "ThreadIndex", "vta"), "coproc_scope", 1): With T.attr(T.iter_var(vta, None, "ThreadIndex", "vta"), "coproc_uop_scope", "VTAPushGEMMOp"): With T.attr(T.iter_var(vta, None, "ThreadIndex", "vta"), "coproc_scope", 2): These variables are multiplicativeįactors over the BLOCK_OUT, BLOCK_IN, and BATCHīy default, the configuration file sets BATCH, BLOCK_IN, andīLOCK_OUT to be 1, 16 and 16 respectively ( BATCH being set toġ implies that our compute building block is vector-matrix multiply). We first define the variables m, n, o to represent The resulting tiled tensor has a shape of (2, 4, 2, 2). Tiling by a (2, 2) tile shape ensures that data within each tile is The diagram below shows the impact of data tiling on a matrix that is ![]() Tiled according to these aforementioned dimension. Thus inferring that the resulting output matrix has aĬonsequently input and output tensors processed by VTA need to be The activation matrix has a (BATCH, BLOCK_IN) shapeĪnd the transposed weight matrix has a (BLOCK_OUT, BLOCK_IN) shape, The dimensions of that matrix-matrix multiplication are specified in Operation per cycle between an activation matrix and a weight matrix,Īdding the result matrix to an accumulator matrix, as shown in the VTA is designed around a tensor core that performs, one matrix-matrix That the data layout matches the layout imposed by the accelerator design. One source of complexity when targeting accelerators is to make sure program_fpga ( remote, bitstream = None ) # In simulation mode, host the RPC server locally. # You can program the FPGA with your own custom bitstream # by passing the path to the bitstream file instead of None. reconfig_runtime ( remote ) # Program the FPGA with a pre-compiled VTA bitstream. connect ( host, port ) # Reconfigure the JIT runtime vta. TARGET = "de10nano" : # Make sure that TVM was compiled with RPC=1 assert tvm. get ( "VTA_RPC_PORT", "9091" )) # We configure both the bitstream and the runtime system on the Pynq # to match the VTA configuration specified by the vta_config.json file. get_env () # We read the Pynq RPC host IP address and port number from the OS environment host = os. From _future_ import absolute_import, print_function import os import tvm from tvm import te import vta import numpy as np from tvm import rpc from tvm.contrib import utils from vta.testing import simulator # Load VTA parameters from the 3rdparty/vta-hw/config/vta_config.json file env = vta.
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