diff --git a/ethosu/vela/architecture_features.py b/ethosu/vela/architecture_features.py
new file mode 100644
index 0000000..4a03d0e
--- /dev/null
+++ b/ethosu/vela/architecture_features.py
@@ -0,0 +1,618 @@
+# 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.
+
+from .nn_graph import MemArea, TensorPurpose, NpuBlockType, TensorFormat
+from .numeric_util import round_up, round_up_divide
+from collections import namedtuple
+from configparser import ConfigParser
+from .supported_operators import SupportedOperators
+import numpy as np
+import enum
+
+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
+    Acc16 = 4
+    Acc32 = 5
+    Acc40 = 6
+    Last = Acc40
+    BitSizes = np.array([8, 16, 8, 16, 16, 32, 40], 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 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 = {
+        "ethos-u55-256": ArchitectureConfig(256, 1, Block(2, 2, 8), Block(2, 2, 8), 48, [8, 8, 8, 8, 8, 16, 20], 8),
+        "ethos-u55-128": ArchitectureConfig(128, 1, Block(2, 1, 8), Block(2, 2, 8), 24, [4, 4, 4, 4, 4, 8, 12], 4),
+        "ethos-u55-64": ArchitectureConfig(64, 1, Block(1, 1, 8), Block(1, 1, 8), 16, [2, 2, 2, 2, 4, 4, 8], 2),
+        "ethos-u55-32": ArchitectureConfig(32, 1, Block(1, 1, 4), Block(1, 1, 8), 16, [2, 2, 2, 2, 4, 4, 4], 1),
+    }
+
+    OFMSplitDepth = 16
+
+    def __init__(
+        self,
+        vela_config: ConfigParser,
+        accelerator_config,
+        system_config,
+        permanent_storage,
+        inter_pass_cycle_delay,
+        dram_bandwidth,
+        override_block_config,
+        block_config_limit,
+        global_memory_clock_scale,
+        max_blockdep,
+    ):
+        accelerator_config = accelerator_config.lower()
+        self.vela_config = vela_config
+        self.accelerator_config = accelerator_config
+        if not self.accelerator_config in ArchitectureFeatures.accelerator_configs:
+            raise Exception("Unknown accelerator configuration " + self.accelerator_config)
+        accel_config = ArchitectureFeatures.accelerator_configs[self.accelerator_config]
+        self.config = accel_config
+
+        self.system_config = system_config
+
+        is_yoda_system = "yoda-" in self.accelerator_config
+
+        if is_yoda_system:
+            self.sram_size = 256 * 1024
+        else:
+            self.sram_size = 200 * 1024 * 1024
+
+        self.ncores = accel_config.cores
+        self.ofm_ublock = accel_config.ofm_ublock
+        self.ifm_ublock = accel_config.ifm_ublock
+        self.subkernel_max = Block(8, 8, 65536)
+        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
+
+        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()
+
+        # 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
+
+        if dram_bandwidth != 0:
+            self.memory_bandwidths_per_cycle[MemArea.Dram] = dram_bandwidth * 1e9 / self.npu_clock
+
+        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.inter_pass_cycle_delay = inter_pass_cycle_delay
+
+        self.default_weight_format = TensorFormat.WeightsCompressed
+        self.default_feature_map_format = TensorFormat.NHWC
+
+        if permanent_storage != MemArea.OffChipFlash:
+            self.permanent_storage_mem_area = permanent_storage
+
+        self.tensor_storage_mem_area = {
+            # permanent mem_area
+            TensorPurpose.Weights: self.permanent_storage_mem_area,
+            TensorPurpose.FeatureMap: self.feature_map_storage_mem_area,
+        }
+
+        self.tensor_load_mem_area = dict(self.tensor_storage_mem_area)
+
+        if self.tensor_storage_mem_area[TensorPurpose.Weights] in (MemArea.OffChipFlash,):
+            self.tensor_load_mem_area[TensorPurpose.Weights] = MemArea.Sram
+
+        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),
+        }
+
+        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),
+        }
+
+        # 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 is_yoda_system:
+            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)
+
+        # 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()
+
+    # 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 SRAM element for a FM of given dimensions
+        size_bytes = (SHRAMElements.BitSizes * (height * width * depth)) // 8
+        # 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 == 8 or ifm_bits == 16
+        assert ifm_depth > 0
+        ifm_depth = round_up(ifm_depth, self.ifm_ublock.depth)
+        max_block_depth = 32 if ifm_bits == 8 else 16
+        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)
+    ):
+        upscaling = 1
+        # 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
+
+        if kernel.stride.y == 1:
+            ifm_block_height = round_up(ifm_block_height, self.ofm_ublock.height)
+        elif kernel.stride.y == 2:
+            if (self.ofm_ublock.height == 2) and (ifm_block_height % 4 == 2):
+                ifm_block_height = ifm_block_height + 2
+            else:
+                ifm_block_height = round_up(ifm_block_height, self.ofm_ublock.height)
+        else:
+            assert False
+
+        # 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
+
+        if kernel.stride.x == 1:
+            ifm_block_width = round_up(ifm_block_width, self.ofm_ublock.width)
+        elif kernel.stride.x == 2:
+            if (self.ofm_ublock.width == 2) and (ifm_block_width % 4 == 2):
+                ifm_block_width = ifm_block_width + 2
+            else:
+                ifm_block_width = round_up(ifm_block_width, self.ofm_ublock.width)
+        else:
+            assert False
+
+        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_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)
+        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
+        ifm_depth = ifm.size().depth
+        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
+        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:
+                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):
+        """
+        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 not section_key in self.vela_config:
+                raise Exception("Unknown system configuration " + self.system_config)
+
+        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 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'). To store the weights and other constant data in SRAM"
+                    " select 'OnChipFlash'"
+                )
+        except:
+            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