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diff --git a/docs/project/2018_requirement_specification.md b/docs/project/2018_requirement_specification.md deleted file mode 100644 index 90e3937ef..000000000 --- a/docs/project/2018_requirement_specification.md +++ /dev/null @@ -1,113 +0,0 @@ -# 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 Smart Machine OS. - -To tackle the challenges above and to have the leadership on on-device AI technology, this project, -as the first step, 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: - -- To support all 50 TensorFlow (TF) Lite operations on ARM CPU and GPU -- To support all 29 operations of Android Neural Network (NN) API on ARM CPU and GPU -- To support InceptionV3 and MobileNet, written in TF Lite model format, on ARM CPU and GPU - - -### Target - -_nnfw_ targets two platforms with two target devices: - -- ODroid XU4 running Tizen 5.0 -- MV8890 running Smart Machine Platform 1.0 - - -### Product Roadmap - -- March: Set up milestones, tasks, workgroups, initial code structure, and build/test infra -- April: Run InceptionV3 using ARM Compute Library (ACL) on ODroid XU4 running Tizen -- May: Run MobileNet on Tizen / Tizen M1 release -- June: Run ADAS models on Tizen -- July: STAR Platform preview release -- October: Tizen M2 release / SMP v1.0 release / STAR Platform v1.0 release - - -## Requirements - -### Functionality Requirements - -_nnfw_ has the following functionality requirements: - -1. Run InceptionV3 on Tizen - - Description - - Support InceptionV3, written in TF Lite model format, on Tizen - - Run on ARM CPU and GPU - - Validation - - Run the test code that executes InceptionV3 on Tizen CPU - - Run the test code that executes InceptionV3 on Tizen GPU - - Compare the results of test codes with that using the TF Lite interpreter -1. Run MobileNet on Tizen - - Description - - Support MobileNet, written in TF Lite model format, on Tizen - - Run on ARM CPU and GPU - - Validation - - Run the test code that executes MobileNet on Tizen CPU - - Run the test code that executes MobileNet on Tizen GPU - - Compare the results of test codes with that using the TF Lite interpreter -1. Support 50 TF Lite operations and 29 NN API operations - - Description - - Support 50 TF Lite operations on Tizen for ARM CPU and GPU - - Support 50 TF Lite operations on SMP for ARM CPU and GPU - - Support 29 NN API operations on Tizen for ARM CPU and GPU - - Support 29 NN API operations on SMP for ARM CPU and GPU - - Validation - - Run the test code for operations on Tizen CPU - - Run the test code for operations on Tizen GPU - - Run the test code for operations on SMP CPU - - Run the test code for operations on SMP GPU - - Compare the results of test codes with that using the TF Lite interpreter - - -### Non-Functionality Requirements - -_nnfw_ does not have non-functionality requirements. |