Enhancing signal reliability through GPU-enabled neural receivers.

Conventional CPU-based receivers struggle with complex, high-volume wireless signals. As networks expand, reliability and throughput are affected.

In order to address this, the Neural Receiver combines CUDA-enabled GPUs with deep learning algorithms for real-time, parallel signal decoding. It optimizes resource utilization in a variety of settings and intelligently adapts to shifting signal conditions.

This accelerator redefines wireless communication. It delivers faster interpretation, lower latency, and superior adaptability, making it perfect for telecom, defense, and IoT applications where every millisecond counts.

Benefits

Real-Time Efficiency

Significantly reduces latency in signal decoding.

Optimized Resource Utilization

Uses GPU parallelism for better energy and performance efficiency.

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Scalability

Adaptable to various communication environments and customer needs.

Enhanced Reliability

AI models improve decoding accuracy and adaptability.

Features

Here are the key features of the accelerator, designed to help you understand the value and capabilities they bring to your projects.

Neural Receivers

AI-driven models for dynamic signal environments, smarter adaptability, and decoding.

CUDA-Powered GPUs

Enables massive parallelism for real-time processing.

CUDA Kernels

Specialized tools for extracting patterns and performing operations.

Modular Architecture

Can be redesigned based on input/output requirements.

Use Cases

Wireless Tech Providers

Supports wireless communication in critical systems, network traffic optimization and management, spectrum management, network fault detection and prediction, and various 6G use cases.
Defence R&D

Enables wireless communication in critical systems, network fault detection and prediction, and spectrum management for mission-critical environments.