NexaSDK for Mobile

Product Introduction

1. Definition: NexaSDK for Mobile is a software development kit (SDK) enabling on-device multimodal AI inference for iOS and Android applications. It falls under the technical category of edge AI frameworks, leveraging hardware acceleration via Apple Neural Engine (iOS) and Qualcomm Hexagon NPU (Android).
2. Core Value Proposition: It eliminates cloud dependency for AI tasks, providing zero cloud costs, complete data privacy, 2x faster inference speeds, and 9× better energy efficiency while running state-of-the-art (SOTA) models directly on mobile devices.

Main Features

1. Hardware-Accelerated Multimodal AI:
   Executes diverse AI models (LLMs, VLMs, ASR, computer vision) using device-specific NPUs/GPUs. The proprietary NexaML engine dynamically optimizes workloads for Qualcomm Hexagon NPU (Android) or Apple Neural Engine (iOS), with CPU/GPU fallback.
2. Unified API for Rapid Integration:
   Implements AI features in 3 code lines via Kotlin/Java (Android) or Swift (iOS) using a builder pattern. Abstracts model deployment complexities like memory management and hardware abstraction.
3. Offline-First Model Hub:
   Supports fully offline execution of SOTA models (embeddings, rerankers, vision-language models) without internet dependency. Models are pre-optimized for mobile NPUs via quantization and kernel fusion.

Problems Solved

1. Pain Point: Cloud-based AI introduces latency, privacy risks (GDPR/HIPAA compliance issues), and unsustainable operational costs from data transmission and API fees.
2. Target Audience:
   • Mobile Developers needing plug-and-play AI for chat/voice features
   • Enterprise Teams requiring HIPAA-compliant on-device processing
   • Product Managers prioritizing battery-efficient AI in low-connectivity regions
3. Use Cases:
   • On-device copilots analyzing local documents/messages offline
   • Privacy-first speech recognition for medical/legal apps
   • Camera-based object detection in manufacturing/retail apps

Unique Advantages

1. Differentiation: Outperforms cloud services (OpenAI, Google ML Kit) with zero latency and 100% data privacy. Surpasses generic mobile ML frameworks (TensorFlow Lite, Core ML) via NPU-specific optimizations and pre-tuned SOTA models.
2. Key Innovation: Proprietary NexaML engine achieves 9× energy efficiency by bypassing CPU bottlenecks via direct NPU memory allocation and asynchronous batching tailored for mobile NPU architectures.

Frequently Asked Questions (FAQ)

1. How does NexaSDK achieve 9× energy efficiency?
   By offloading 95% of AI computations to NPUs via hardware-specific kernels, reducing CPU wake time and minimizing power-intensive data transfers.
2. What models work with NexaSDK for Mobile?
   Supports quantized LLMs (e.g., Phi-2), vision-language models (CLIP), ASR (Whisper-tiny), and custom ONNX/TFLite models optimized for mobile NPUs.
3. Is internet required for NexaSDK’s AI features?
   No. All inference occurs on-device with no data transmitted to servers, ensuring full offline functionality.
4. How does NexaSDK handle iOS/Android fragmentation?
   Automatically detects device capabilities (NPU/GPU/CPU) and selects optimal execution backend via its hardware abstraction layer.
5. Can enterprises deploy proprietary models with NexaSDK?
   Yes. Supports private model deployment with end-to-end encryption and offline license management for enterprise security.