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|
# Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved.
#
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the License); you may
# not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an AS IS BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Description:
# Holds a container for Ethos-U55/System architecture parameters.
import enum
from collections import namedtuple
from configparser import ConfigParser
import numpy as np
from .errors import OptionError
from .ethos_u55_regs.ethos_u55_regs import resampling_mode
from .numeric_util import round_up
from .numeric_util import round_up_divide
from .operation import NpuBlockType
from .supported_operators import SupportedOperators
from .tensor import MemArea
from .tensor import MemType
from .tensor import TensorFormat
from .tensor import TensorPurpose
PointXY = namedtuple("PointXY", "x y")
PointXYZ = namedtuple("PointXYZ", "x y z")
class Block:
def __init__(self, w, h, d):
self.width = w
self.height = h
self.depth = d
def __eq__(self, other):
if self.width == other.width and self.height == other.height and self.depth == other.depth:
return True
else:
return False
def __repr__(self):
return "<Block: {0},{1},{2}>".format(self.width, self.height, self.depth)
@classmethod
def from_string(cls, s):
w, h, c = (int(v) for v in s.split("x"))
return cls(w, h, c)
class Rect:
def __init__(self, x, y, z, x2, y2, z2):
self.x = x
self.y = y
self.z = z
self.x2 = x2
self.y2 = y2
self.z2 = z2
def start(self):
return PointXYZ(self.x, self.y, self.z)
def end(self):
return PointXYZ(self.x2, self.y2, self.z2)
def size(self):
return Block(self.x2 - self.x + 1, self.y2 - self.y + 1, self.z2 - self.z + 1)
def __repr__(self):
return "<Rect: ({0},{1},{2}) ({3},{4},{5})>".format(self.x, self.y, self.z, self.x2, self.y2, self.z2)
class Kernel:
def __init__(self, w, h, sx=1, sy=1, dx=1, dy=1):
assert sx > 0 and sy > 0
assert dx > 0 and dy > 0
self.width = w
self.height = h
self.stride = PointXY(sx, sy)
self.dilation = PointXY(dx, dy)
class SHRAMElements:
IFM8 = 0
IFM16 = 1
IFM8_Elementwise = 2
IFM16_Elementwise = 3
IFM32 = 4
Acc16 = 5
Acc32 = 6
Acc40 = 7
Last = Acc40
BitSizes = np.array([8, 16, 8, 16, 32, 16, 32, 40], np.int32)
ByteSizes = BitSizes // 8
PostAlign = np.array([8, 8, 8, 8, 8, 1, 1, 1], np.int32)
PreAlign = np.array([1, 1, 1, 1, 1, 8, 8, 8], np.int32)
class SHRAMBlockConfig:
def __init__(self, sizes, banks):
assert len(banks) == SHRAMElements.Last + 1
self.sizes = sizes
self.banks = banks
# Area indices must match Ethos-U55 SHRAM layout spec
class SharedBufferArea(enum.IntEnum):
OFM = 0
Weights = 1
IFM = 2
Accumulators = 3
Size = Accumulators + 1
class Accelerator(enum.Enum):
Ethos_U55_32 = "ethos-u55-32"
Ethos_U55_64 = "ethos-u55-64"
Ethos_U55_128 = "ethos-u55-128"
Ethos_U55_256 = "ethos-u55-256"
Yoda_256 = "yoda-256"
Yoda_512 = "yoda-512"
@classmethod
def member_list(cls):
return [e.value for e in cls]
class ArchitectureFeatures:
"""This class is a container for various parameters of the Ethos-U55 core
and system configuration that can be tuned, either by command line
parameters or by the Ethos-U55 architects. The class is often passed
around to passes that need to do architecture-dependent actions.
Note the difference between ArchitectureFeatures and CompilerOptions
- ArchitectureFeatures is for changing the Ethos-U55 and system architecture
- CompilerOptions is for changing the behaviour of the compiler
"""
ArchitectureConfig = namedtuple(
"ArchitectureConfig", "macs cores ofm_ublock ifm_ublock shram_banks shram_granules elem_units"
)
accelerator_configs = {
Accelerator.Yoda_512: ArchitectureConfig(
256, 2, Block(2, 2, 8), Block(2, 2, 8), 48, [8, 8, 8, 8, 16, 8, 16, 20], 8
),
Accelerator.Yoda_256: ArchitectureConfig(
256, 1, Block(2, 2, 8), Block(2, 2, 8), 48, [8, 8, 8, 8, 16, 8, 16, 20], 8
),
Accelerator.Ethos_U55_256: ArchitectureConfig(
256, 1, Block(2, 2, 8), Block(2, 2, 8), 48, [8, 8, 8, 8, 16, 8, 16, 20], 8
),
Accelerator.Ethos_U55_128: ArchitectureConfig(
128, 1, Block(2, 1, 8), Block(2, 2, 8), 24, [4, 4, 4, 4, 8, 4, 8, 12], 4
),
Accelerator.Ethos_U55_64: ArchitectureConfig(
64, 1, Block(1, 1, 8), Block(1, 1, 8), 16, [2, 2, 2, 2, 4, 4, 4, 8], 2
),
Accelerator.Ethos_U55_32: ArchitectureConfig(
32, 1, Block(1, 1, 4), Block(1, 1, 8), 16, [2, 2, 2, 2, 4, 4, 4, 4], 1
),
}
OFMSplitDepth = 16
SubKernelMax = Block(8, 8, 65536)
def __init__(
self,
vela_config: ConfigParser,
accelerator_config,
system_config,
override_block_config,
block_config_limit,
global_memory_clock_scale,
max_blockdep,
weight_estimation_scaling,
):
accelerator_config = accelerator_config.lower()
self.vela_config = vela_config
if accelerator_config not in Accelerator.member_list():
raise OptionError("--accelerator-config", self.accelerator_config, "Unknown accelerator configuration")
self.accelerator_config = Accelerator(accelerator_config)
accel_config = ArchitectureFeatures.accelerator_configs[self.accelerator_config]
self.config = accel_config
self.system_config = system_config
self.is_yoda_system = self.accelerator_config in (Accelerator.Yoda_256, Accelerator.Yoda_512)
self.max_outstanding_dma = 2 if self.is_yoda_system else 1
self.max_outstanding_kernels = 3
self.ncores = accel_config.cores
self.ofm_ublock = accel_config.ofm_ublock
self.ifm_ublock = accel_config.ifm_ublock
self.ofm_block_max = Block(64, 32, 128)
self.override_block_config = override_block_config
self.block_config_limit = block_config_limit
self.global_memory_clock_scale = global_memory_clock_scale
if self.global_memory_clock_scale <= 0.0 or self.global_memory_clock_scale > 1.0:
raise Exception(
"Invalid global_memory_clock_scale = "
+ str(self.global_memory_clock_scale)
+ " (must be > 0.0 and <= 1.0)"
)
self.max_blockdep = max_blockdep
self.weight_estimation_scaling = weight_estimation_scaling
dpu_min_height = accel_config.ofm_ublock.height
dpu_min_width = accel_config.ofm_ublock.width
dpu_dot_product_width = 8
dpu_min_ofm_channels = accel_config.ofm_ublock.depth
self.num_elem_wise_units = accel_config.elem_units
self.num_macs_per_cycle = dpu_min_height * dpu_min_width * dpu_dot_product_width * dpu_min_ofm_channels
self.memory_clock_scales = np.zeros(MemArea.Size)
self.memory_port_widths = np.zeros(MemArea.Size)
# Get system configuration
self.__read_sys_config(self.is_yoda_system)
# apply the global memory clock scales to the individual ones from the system config
for mem in MemArea.all():
self.memory_clock_scales[mem] *= self.global_memory_clock_scale
self.memory_clocks = self.memory_clock_scales * self.npu_clock
self.memory_bandwidths_per_cycle = self.memory_port_widths * self.memory_clock_scales / 8
self.memory_bandwidths_per_second = self.memory_bandwidths_per_cycle * self.npu_clock
# sizes as N x H x W x C. we need to round up to these when allocating storage
self.storage_rounding_quantums = {
TensorFormat.Unknown: (1, 1, 1, 1),
TensorFormat.WeightsCompressed: (1, 1, 1, 1),
TensorFormat.NHWC: (1, 1, 1, 1),
TensorFormat.NHCWB16: (1, 1, 1, 16),
}
# brick sizes as N x H x W x C. We have to fetch whole bricks at a time
self.brick_sizes = {
TensorFormat.Unknown: (1, 1, 1, 1),
TensorFormat.WeightsCompressed: (1, 1, 1, 1),
TensorFormat.NHWC: (1, 1, 1, 1),
TensorFormat.NHCWB16: (1, 1, 1, 16),
}
self.default_weight_format = TensorFormat.WeightsCompressed
self.default_feature_map_format = TensorFormat.NHWC
self.tensor_storage_mem_area = {
# permanent mem_area
TensorPurpose.Unknown: MemArea.Unknown,
TensorPurpose.Weights: self.permanent_storage_mem_area,
TensorPurpose.FeatureMap: self.feature_map_storage_mem_area,
TensorPurpose.LUT: self.permanent_storage_mem_area,
}
self.tensor_storage_mem_type = {
TensorPurpose.Unknown: MemType.Unknown,
TensorPurpose.Weights: MemType.Permanent_NPU,
TensorPurpose.FeatureMap: MemType.Scratch,
TensorPurpose.LUT: MemType.Scratch,
}
self.min_block_sizes = {
NpuBlockType.Default: (dpu_min_height, dpu_min_width),
NpuBlockType.VectorProduct: (1, 1),
NpuBlockType.ConvolutionMxN: (dpu_min_height, dpu_min_width),
NpuBlockType.Pooling: (dpu_min_height, dpu_min_width),
NpuBlockType.ConvolutionDepthWise: (dpu_min_height, dpu_min_width),
NpuBlockType.ElementWise: (1, 1),
NpuBlockType.ReduceSum: (dpu_min_height, dpu_min_width),
}
self.sub_kernel_limits = {
NpuBlockType.Default: (8, 8),
NpuBlockType.VectorProduct: (1, 1),
NpuBlockType.ConvolutionMxN: (8, 8),
NpuBlockType.Pooling: (8, 8),
NpuBlockType.ConvolutionDepthWise: (8, 8),
NpuBlockType.ElementWise: (1, 1),
NpuBlockType.ReduceSum: (8, 8),
}
# weights for scheduler search
from .npu_performance import make_bandwidth_array
self.bandwidth_weights = make_bandwidth_array()
self.bandwidth_weights[MemArea.Sram] = 1.0
self.bandwidth_weights[MemArea.Dram] = 10.0
self.bandwidth_weights[MemArea.OnChipFlash] = 2.0
self.bandwidth_weights[MemArea.OffChipFlash] = 20.0
self.cycles_weight = 40
self.max_sram_used_weight = 1000
if self.is_yoda_system and (self.fast_storage_mem_area != self.feature_map_storage_mem_area):
self.max_sram_used_weight = 0
# Shared Buffer Block allocations
self.shram_bank_size = 1024 # bytes
self.shram_size_bytes = accel_config.shram_banks * self.shram_bank_size
self.shram_reserved_output_banks = 2
self.shram_reserved_weight_banks = 0
self.shram_reserved_unused_banks = 2 if accel_config.shram_banks > 16 else 0
self.shram_total_banks = accel_config.shram_banks - self.shram_reserved_unused_banks
self.shram_bank_granules = np.array(accel_config.shram_granules, np.int32)
self.shram_lut_size = 2048
# SHRAM base address of the activation lookup table
self.shram_lut_address = self.shram_bank_size * self.available_shram_banks(True)
# Build a map of acceptable IFM/OFM block configurations up to the maximum
# IFM/OFM block size.
ifm_block_max = self.get_ifm_block_size(32, self.ofm_block_max, Kernel(8, 8))
self.block_config_map = dict()
self.generate_block_config_map(Block(ifm_block_max.width, ifm_block_max.height, 128))
# Setup supported operators and restriction checkers class
self.supported_operators = SupportedOperators()
# Returns available number of SHRAM banks depending on activation lookup table
# being used or not
def available_shram_banks(self, uses_activation_lut):
banks = self.shram_total_banks
if uses_activation_lut and self.shram_reserved_unused_banks == 0:
banks -= 2
return banks
# Calculate block configuration for ALL known IFM operations and
# accumulator sizes. Consumers will need to select their preferred
# operation and bit-width at read-time.
def generate_block_config(self, width, height, depth):
# Number of bytes required for any SHRAM element for a FM of given dimensions.
# For IFM: size = H*W*Align(D*BYTE_WIDTH, 8)
# For ACC: size = H*W*Align(D,8)*BYTE_WIDTH
d1 = round_up(depth, SHRAMElements.PreAlign)
d2 = round_up(d1 * SHRAMElements.ByteSizes, SHRAMElements.PostAlign)
size_bytes = (height * width) * d2
# Convert byte size (rounded) to size in banks
size_banks = round_up_divide(size_bytes, self.shram_bank_size)
size_banks *= 2 # Double buffer the IFM/Acc (need twice as many banks)
# Round bank requirement to bank granularity
required_banks = round_up(size_banks, self.shram_bank_granules)
return SHRAMBlockConfig(size_bytes, required_banks)
@staticmethod
def make_block_config_key(width, height, depth):
return (int(height), int(width), int(depth))
def get_block_config(self, width, height, depth):
assert depth <= self.ofm_block_max.depth
key = ArchitectureFeatures.make_block_config_key(width, height, depth)
config = self.block_config_map.get(key, None)
return config
# Generate a key:value map of possible block configurations, where the
# key is compounded from the block dimensions: 0x00HHWWCC
def generate_block_config_map(self, block: Block):
for h in range(1, block.height + 1):
for w in range(1, block.width + 1):
# All possible IFM/OFM depth values
for c in [4, 8, 12, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128]:
key = ArchitectureFeatures.make_block_config_key(w, h, c)
self.block_config_map[key] = self.generate_block_config(w, h, c)
def calc_ifm_block_depth(self, ifm_depth, ifm_bits):
assert ifm_bits in (8, 16, 32)
assert ifm_depth > 0
ifm_depth = round_up(ifm_depth, self.ifm_ublock.depth)
max_block_depth = 8 * 32 // ifm_bits
return min(max_block_depth, ifm_depth)
# Calculate the size of the IFM block given a depth, target OFM block and a kernel
def get_ifm_block_size(
self,
ifm_block_depth,
ofm_block: Block,
kernel: Kernel,
subkernel: Block = Block(8, 8, 65536),
ifm_resampling_mode=resampling_mode.NONE,
):
upscaling = 1 if ifm_resampling_mode == resampling_mode.NONE else 2
# Height
ifm_odd_2x_height_enable = 0
dilated_kernel_height = ((kernel.height - 1) * kernel.dilation.y) + 1
ifm_block_height = (
(ofm_block.height - 1) * kernel.stride.y
+ min(subkernel.height, dilated_kernel_height)
+ ifm_odd_2x_height_enable
) // upscaling
ifm_block_height = round_up(ifm_block_height, self.ofm_ublock.height)
# Width
ifm_odd_2x_width_enable = 0
dilated_kernel_width = ((kernel.width - 1) * kernel.dilation.x) + 1
ifm_block_width = (
(ofm_block.width - 1) * kernel.stride.x
+ min(subkernel.width, dilated_kernel_width)
+ ifm_odd_2x_width_enable
) // upscaling
ifm_block_width = round_up(ifm_block_width, self.ofm_ublock.width)
return Block(ifm_block_width, ifm_block_height, ifm_block_depth)
@staticmethod
def intersects(start_a, end_a, start_b, end_b):
start_x = max(start_a[0], start_b[0])
end_x = min(end_a[0], end_b[0])
start_y = max(start_a[1], start_b[1])
end_y = min(end_a[1], end_b[1])
start_z = max(start_a[2], start_b[2])
end_z = min(end_a[2], end_b[2])
return ((end_x - start_x) > 0) and ((end_y - start_y) > 0) and ((end_z - start_z) > 0)
# Block job dependency:
# Does the VOLUME of IFMs for block job B(0) overlap with VOLUME of OFMs block jobs A(8,9,10)
#
# A | B
# ----------------------+------------------
# .... 3,4,5,6,7,8,9,10 | 0,1,2,3,4,5,6,8 10 < JOB NUMBER
# |<------->| dependency offset
#
MAX_BLOCKDEP = 3
# Get the coordinates of a block offset from either the end (negative)
# or the start (zero or positive) of the given 3d area
def get_offset_block_coords(self, area: Rect, block: Block, offset):
size = area.size()
# Dimensions of the region, in blocks
width_blocks = round_up_divide(size.width, block.width)
height_blocks = round_up_divide(size.height, block.height)
depth_blocks = round_up_divide(size.depth, block.depth)
total_blocks = width_blocks * height_blocks * depth_blocks
if offset < 0:
index = total_blocks + offset
else:
index = offset
if index >= total_blocks:
return None
# Coordinates of the indexed block
coord_z = block.depth * (index % depth_blocks)
coord_y = block.height * (index // (depth_blocks * width_blocks))
coord_x = block.width * ((index // depth_blocks) % width_blocks)
return (coord_x + area.x, coord_y + area.y, coord_z + area.z)
def get_first_job_input_volume(
self, ifm: Rect, ofm: Rect, ifm_block_depth, ofm_block: Block, kernel: Kernel, padLT, block_offset
):
# Get ifm block size (jobs are invisibly decomposed into subkernels)
ifm_block = self.get_ifm_block_size(ifm_block_depth, ofm_block, kernel, self.ofm_block_max)
ifm_depth_blocks = round_up_divide(ifm.size().depth, ifm_block_depth)
# Which OFM block are we calculating
ofm_coord = self.get_offset_block_coords(ofm, ofm_block, block_offset // ifm_depth_blocks)
if ofm_coord is None:
return None
# Coordinate of the source IFM block
ifm_coord_x = max(0, ofm_coord[0] * kernel.stride.x - padLT[0])
ifm_coord_y = max(0, ofm_coord[1] * kernel.stride.y - padLT[1])
ifm_coord_z = ifm.z + (block_offset % ifm_depth_blocks) * ifm_block.depth
# IFM block that will be sampled for the FIRST+block_offset job in the next operator's OFM
start_coord = (ifm_coord_x, ifm_coord_y, ifm_coord_z)
end_coord = (
start_coord[0] + ifm_block.width,
start_coord[1] + ifm_block.height,
start_coord[2] + ifm_block.depth,
)
return (start_coord, end_coord, 1) # start, end, total jobs
def get_prev_job_output_volume(
self, ifm: Block, ofm: Rect, ifm_block_depth, ofm_block: Block, kernel: Kernel, block_offset
):
assert block_offset >= 0
# Get OFM block's volume coordinates
start_coord = self.get_offset_block_coords(ofm, ofm_block, -1 - block_offset)
if start_coord is None:
return None
end_coord = (
start_coord[0] + ofm_block.width,
start_coord[1] + ofm_block.height,
start_coord[2] + ofm_block.depth,
)
# Calculate how many IFM blocks this OFM block requires (i.e how many jobs)
ifm_depth_blocks = round_up_divide(ifm.size().depth, ifm_block_depth)
ifm_depth_blocks = 1 # Overwrite with 1 to force OFM block dependency, not IFM
return (start_coord, end_coord, ifm_depth_blocks) # start, end, total jobs for this OFM block
def calc_block_dep(
self,
prev_ifm: Block,
prev_ofm: Block,
prev_ifm_block_depth,
prev_ofm_block: Block,
prev_kernel: Kernel,
ifm: Block,
ofm: Block,
ifm_block_depth,
ofm_block: Block,
kernel: Kernel,
padLT,
):
blockdep = ArchitectureFeatures.MAX_BLOCKDEP
# Iterate over the next BLOCKDEP inputs, checking to see if a sliding window
# of IFM area overlaps with any previous OFM block generation.
elapsed_jobs = 0
for forward_offset in range(ArchitectureFeatures.MAX_BLOCKDEP):
# This is the IFM block we want to sample from
in_area = self.get_first_job_input_volume(
ifm, ofm, ifm_block_depth, ofm_block, kernel, padLT, forward_offset
)
if in_area is None:
break
# Try several previous-OFM blocks in the past (they still might comprise multiple IFM jobs)
outstanding_jobs = 0
for block_offset in range(ArchitectureFeatures.MAX_BLOCKDEP):
# This is the OFM block being generated by the previous op
out_area = self.get_prev_job_output_volume(
prev_ifm, prev_ofm, prev_ifm_block_depth, prev_ofm_block, prev_kernel, block_offset
)
if out_area is None:
break
# Block dependency is the max number of allowed outstanding jobs
# in the pipeline. Selected by determining how many jobs occur
# in between two operators' overlapping OFM->IFM block volumes
if ArchitectureFeatures.intersects(in_area[0], in_area[1], out_area[0], out_area[1]):
break
# Early exit if no intersections and we've seen enough jobs in the pipeline
elif outstanding_jobs > ArchitectureFeatures.MAX_BLOCKDEP:
break
# This OFM had this many jobs (accumulate over multiple OFM blocks)
outstanding_jobs += out_area[2]
blockdep = min(blockdep, elapsed_jobs + outstanding_jobs)
elapsed_jobs += in_area[2]
# Early exit if no intersections and we've seen enough jobs in the pipeline
if elapsed_jobs > ArchitectureFeatures.MAX_BLOCKDEP:
break
return blockdep
def cpu_cycle_estimate(self, op):
"""
Gets estimated performance of a CPU operation, based on a linear model of intercept, slope,
specified in the vela config file, in ConfigParser file format (.ini file).
Example configuration snippet:
[CpuPerformance.MyOperationType]
Cortex-Mx.intercept=<some float value>
Cortex-Mx.slope=<some float value>
"""
section = "CpuPerformance." + op.type.name
if self.vela_config is not None and section in self.vela_config:
op_config = self.vela_config[section]
try:
intercept = float(op_config.get(self.cpu_config + ".intercept", op_config["default.intercept"]))
slope = float(op_config.get(self.cpu_config + ".slope", op_config["default.slope"]))
n_elements = op.inputs[0].elements()
cycles = intercept + n_elements * slope
return cycles
except Exception:
print("Error: Reading CPU cycle estimate in vela configuration file, section {}".format(section))
raise
print("Warning: No configured CPU performance estimate for", op.type)
return 0
def __read_sys_config(self, is_yoda_system):
"""
Gets the system configuration with the given name from the vela configuration file
Example configuration snippet:
[SysConfig.MyConfigName]
npu_freq=<some float value>
cpu=Cortex-Mx
...
"""
# Get system configuration from the vela configuration file
if self.vela_config is None:
print("Warning: Using default values for system configuration")
else:
section_key = "SysConfig." + self.system_config
if section_key not in self.vela_config:
raise OptionError("--system-config", self.system_config, "Unknown system configuration")
try:
self.npu_clock = float(self.__sys_config("npu_freq", "500e6"))
self.cpu_config = self.__sys_config("cpu", "Cortex-M7")
self.memory_clock_scales[MemArea.Sram] = float(self.__sys_config("Sram_clock_scale", "1"))
self.memory_port_widths[MemArea.Sram] = int(self.__sys_config("Sram_port_width", "64"))
self.memory_clock_scales[MemArea.OnChipFlash] = float(self.__sys_config("OnChipFlash_clock_scale", "1"))
self.memory_port_widths[MemArea.OnChipFlash] = int(self.__sys_config("OnChipFlash_port_width", "64"))
self.memory_clock_scales[MemArea.OffChipFlash] = float(
self.__sys_config("OffChipFlash_clock_scale", "0.25")
)
self.memory_port_widths[MemArea.OffChipFlash] = int(self.__sys_config("OffChipFlash_port_width", "32"))
self.memory_clock_scales[MemArea.Dram] = float(self.__sys_config("Dram_clock_scale", "1"))
self.memory_port_widths[MemArea.Dram] = int(self.__sys_config("Dram_port_width", "32"))
self.fast_storage_mem_area = MemArea[self.__sys_config("fast_storage_mem_area", "Sram")]
self.feature_map_storage_mem_area = MemArea[self.__sys_config("feature_map_storage_mem_area", "Sram")]
self.permanent_storage_mem_area = MemArea[self.__sys_config("permanent_storage_mem_area", "OffChipFlash")]
if is_yoda_system:
if self.permanent_storage_mem_area is not MemArea.Dram:
raise Exception(
"Invalid permanent_storage_mem_area = "
+ str(self.permanent_storage_mem_area)
+ " (must be 'DRAM' for Yoda)."
)
else:
if self.permanent_storage_mem_area not in set((MemArea.OnChipFlash, MemArea.OffChipFlash)):
raise Exception(
"Invalid permanent_storage_mem_area = "
+ str(self.permanent_storage_mem_area)
+ " (must be 'OnChipFlash' or 'OffChipFlash' for ethosu-55)."
" To store the weights and other constant data in SRAM on ethosu-55 select 'OnChipFlash'"
)
self.sram_size = 1024 * int(self.__sys_config("sram_size_kb", "204800"))
except Exception:
print("Error: Reading System Configuration in vela configuration file, section {}".format(section_key))
raise
def __sys_config(self, key, default_value):
"""
Gets the system configuration value with the given key from the vela config file.
"""
if self.vela_config is None:
return default_value
section = "SysConfig." + self.system_config
result = self.vela_config[section].get(key, None)
if result is None:
raise Exception("Error: System Configuration Missing key {} in section [{}] ".format(key, section))
return result
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