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diff --git a/src/dynamic_fusion/sketch/gpu/GpuKernelComponentGraph.cpp b/src/dynamic_fusion/sketch/gpu/GpuKernelComponentGraph.cpp
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+/*
+ * Copyright (c) 2022 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "GpuKernelComponentGraph.h"
+
+#include "arm_compute/dynamic_fusion/sketch/MemoryDescriptor.h"
+
+namespace arm_compute
+{
+namespace experimental
+{
+namespace dynamic_fusion
+{
+namespace
+{
+/** Automatically create memory descriptors for all tensors in the graph
+ *
+ * @param[in] tensors @ref ITensorInfo map
+ * @param[in] graph   @ref DependencyGraph of which the @p tensors are a part
+ *
+ * @return MemoryDescriptorMap  An assignment map of @ref MemoryDescriptors for each ITensorInfo in the graph
+ */
+MemoryDescriptorMap assign_memory_descriptors(const std::map<ITensorInfo::Id, const ITensorInfo *> tensors, const DependencyGraph &graph)
+{
+    MemoryDescriptorMap mem_map{};
+    for(auto t_id : graph.all_tensors())
+    {
+        const auto &tensor = tensors.at(t_id);
+        // Only global src and dst tensors to the entire component graph are "User" tensors, which are user-specified memories
+        if(is_in(t_id, graph.global_src_tensors()) || is_in(t_id, graph.global_dst_tensors()))
+        {
+            mem_map[t_id] = MemoryDescriptor{ MemoryType::User };
+        }
+        else
+        {
+            AuxMemoryInfo aux_mem_info{ tensor->total_size() };
+            mem_map[t_id] = MemoryDescriptor{ MemoryType::Auxiliary, aux_mem_info };
+        }
+    }
+    return mem_map;
+}
+
+} // namespace
+
+std::vector<DependencyGraph::TensorId> GpuKernelComponentGraph::get_tensor_ids(const std::vector<const ITensorInfo *> tensors)
+{
+    std::vector<DependencyGraph::TensorId> tensor_ids{};
+    std::transform(
+        std::begin(tensors), std::end(tensors),
+        std::back_inserter(tensor_ids),
+        [](const auto & t)
+    {
+        return t->id();
+    });
+    return tensor_ids;
+}
+
+GpuKernelComponentGraph::GpuKernelComponentGraph(GpuComponentServices *services)
+    : _services{ services }, _components{}, _tensors{}, _dependency_graph{}
+{
+}
+
+GpuKernelComponentStream GpuKernelComponentGraph::fuse() const
+{
+    // Obtain memory descriptor map
+    const auto mem_map = assign_memory_descriptors(_tensors, _dependency_graph);
+    /// @note Fusion constraints (for kernel components) are exactly the same as the invariants of @ref GpuKernelComponentGroup
+    /// Fusion can be framed as a mathematical optimization problem:
+    /// Given fusion constraints, find the "best" fusion patterns possible
+    /// "Best" is ill-defined at the moment. For now we define "best" fusion pattern as one
+    /// which results in the least number of fused kernels ( @ref GpuKernelComponentGroup ) at the end
+
+    /// As the first iteration, we offer a sub-optimal algorithm here which ensures all
+    /// constraints are met, but provides no guarantee that the fusion pattern is optimal
+
+    GpuKernelComponentStream stream{ _services, mem_map };
+    // Break down into linear groups of components (constraint 1), preserving topological order
+    const auto linear_graphs = _dependency_graph.topological_partition();
+
+    // Further divide up the linear groups based on rest of the fusion constraints (rely on component group's invariants)
+    for(const auto &graph : linear_graphs)
+    {
+        for(unsigned int i = 0; i < graph.size(); ++i)
+        {
+            const auto comp = _components.at(graph[i].op).get();
+            // Each new linear graph signals a new component group in the stream
+            if(i == 0)
+            {
+                stream.new_component_group();
+            }
+            // If it violates the component group's invariant / fusion constraint, breaks up the stream by inserting a new group
+            bool success = stream.add_component(comp);
+            if(!success)
+            {
+                stream.new_component_group();
+                success = stream.add_component(comp);
+                ARM_COMPUTE_ERROR_ON(!success);
+            }
+        }
+    }
+    return stream;
+}
+} // namespace dynamic_fusion
+} // namespace experimental
+} // namespace arm_compute