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diff --git a/docs/project/2018_requirement_specification.md b/docs/project/2018_requirement_specification.md new file mode 100644 index 000000000..90e3937ef --- /dev/null +++ b/docs/project/2018_requirement_specification.md @@ -0,0 +1,113 @@ +# 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. |