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+# Software Requirement Specification
+
+## Background
+Artificial intelligence (AI) techniques are getting popular and utilized in various products and
+services.  While the cloud-based AI techniques have been used to perform compute/memory intensive
+inferences because of the powerful servers on cloud, on-device AI technologies are recently drawing
+attention from the mobile industry for response time reduction, privacy protection, and
+connection-less AI service.  Big mobile players, such as Google, Apple, and Huawei, are investing
+their research effort on the on-device AI technologies and already announced hardware and software
+on-device AI solutions.  Samsung is not leading this trend currently, but since on-device AI area is
+just started and still in the initial state, there are still opportunities and possibilities to
+reduce the gap between pioneer companies and Samsung.  We believe on-device AI will become a key
+differentiator for mobile phone, TV, and other home appliances, and thus developing on-device AI
+software stack is of paramount importance in order to take leadership in the on-device AI
+technology.
+
+Although the vision of on-device AI is promising, enabling on-device AI involves unique technical
+challenges compared to traditional cloud-based approach.  This is because on-device AI tries to
+conduct inference tasks solely on device without connecting to cloud resources.  Specifically,
+hardware resources on device, such as processor performance, memory capacity, and power budget, are
+very scarce and limit the compute capability, which is typically required to execute complicated
+neural network (NN) models.  For example, in one product requirement, a mobile device should consume
+less than 1.2W and could use at most 2W only for 10 minutes due to thermal issue.  Next, on-device
+AI software stack needs to support diverse device environments, since embedded platforms may consist
+of heterogeneous compute devices, such as CPU, GPU, DSP, or neural processing unit (NPU), and use
+different OS platforms, such as Tizen, Android, or various embedded Linux.
+
+To tackle the challenges above and to have the leadership on on-device AI technology, this project
+aims at developing a neural network inference framework specialized and optimized for on-device AI.
+
+
+## Product Context
+
+This project _nnfw_ aims at providing a high-performance, on-device neural network (NN) inference
+framework that performs inference of a given NN model on processors, such as CPU, GPU, or NPU, in
+the target platform, such as Tizen and Smart Machine Platform (SMP).
+
+### Expected Value
+
+We expect the following would be possible with _nnfw_:
+
+- To improve user experience by reducing the service response time.
+- To provide AI services without network connection while achieving similar performance.
+- To protect personal information and company confidential by limiting data transfer to the network.
+
+
+### Success Criteria
+
+The goals of this project are:
+
+- Supports mixed acceleration using CPU and GPU.
+  + for operator coverage flexibility.
+  + for flexible utilization of computing resource on device.
+- Define Common IR and Runtime APIs and perform successful inference using it.
+  + for _nncc_ integration as a frontend, and _nnfw_ itself to concentrate on the backend.
+- Support of user implemented kernel extension for custom operator.
+- Construction of SW infrastructure to support SR NPU.
+
+
+### Target
+
+_nnfw_ targets following platforms and target devices:
+
+- Odroid-XU4 running Tizen 5.5 (Primary)
+- A variety of Android based mobile phones (Secondary)
+
+
+### Product Roadmap
+
+- March: Set up milestones, tasks, workgroups, initial code structure, and build/test
+  infrastructure.
+- May: Tizen M1 release / Execute InceptionV3 with static scheduling by mixing CPU and GPU on
+  Odroid-XU4.
+- August: Perform inference using NN Package and Runtime API.
+- October: Tizen M2 release / Completed neural network acceleration SW stack integrated with NN
+  Compiler.
+- December: Release NPU SDK v1.0 pre-alpha.
+
+
+## Requirements
+
+### Functionality Requirements
+
+_nnfw_ has the following functionality requirements:
+
+1. CPU/GPU mixed acceleration
+    - Description
+      + Run the model using a mixture of CPU and GPU
+    - Validation
+      + Run the InceptionV3 model by selecting CPU or GPU for individual operators.
+      + Confirm execution results against ground truth(for example, the result of using CPU or GPU
+        alone).
+1. Support its own input format
+   - Description
+      + Define and support its own input format to ensure file format independence
+      + Define and implement of Common IR
+   - Validation
+      + Read and execute the input model described by Common IR.
+      + Confirm execution results against ground truth(for example, the result of using NNAPI).
+1. Support its own API
+   - Description
+      + Define and implement its own API to replace current NNAPI.
+   - Validation
+      + Perform unit tests and integration tests for individual APIs.
+1. Custom operator support
+   - Description
+      + Defines a specification that describes a custom operation, and provides a mechanism for
+        writing and installing the kernel implementation needed to run it at runtime.
+   - Validation
+      + Load and execute the input format that contains the model using the custom op and the kernel
+        implementation of the custom op.
+      + Confirm execution results against ground truth.
+1. Prepare SW infrastructure for NPU support
+    - Description
+      + The runtime must be able to read and process the model information developed for the purpose
+        of execution in NPU.
+   - Validation
+      + Read the model developed for the NPU and run it on the NPU.
+      + Confirm execution results against ground truth.
+
+
+### Non-Functionality Requirements
+
+1. Optimizing mixed acceleration performance
+    - Description
+      + Ensure performance above the individual acceleration averages for mixed acceleration using
+        CPU and GPU.
+    - Validation
+      + Measure the inference time for mixed accelerations for the target model.
+      + Compare the result to the average value of the CPU or GPU alone acceleration time for the
+        same model.