Page 308 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

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Item Details
Model Training and This architecture enables AI and wireless baseband integration.
Fine-Tuning
Testbeds or Pilot The Echo Testbench serves as an open-source wireless testbed providing
Deployments a full-stack development environment [4]

The source codes for the Echo computing architecture, Venus LLVM tool-
Code repositories kit for the construction of highly optimized compilers, optimizers, and
run-time environments, and Zoozve, a compiler, are open-sourced [2][3][4].

2 Use Case Description

2�1 Description

Background and Motivation

The mobile communications industry faces a sustainability crisis. While 5G delivers performance
improvements, it has failed to catalyze disruptive applications, leaving operators burdened
with exorbitant deployment costs. Industry leaders have even voiced skepticism about 6G. This
dilemma stems fundamentally from the closed nature of traditional baseband architectures:
hardware relies on rigid ASICs or DSPs, and software ecosystems remain proprietary, creating
three critical contradictions—limited hardware choices stifle cross-domain innovation, the
absence of open-source platforms impedes academia-industry collaboration, and protracted
development cycles cannot keep pace with 6G’s rapid evolution. The essence of 6G lies in
transforming AI from a "service carried by networks" to a "native network capability," manifesting
as dual paradigms: AI4NET (end-to-end AI optimization of air interfaces and operations)
and NET4AI (native integration of communication-computing-sensing capabilities within
network elements). This convergence demands architectural reinvention that supports large-
scale distributed AI applications while embedding trustworthiness—encompassing security,
privacy, and resilience—as foundational attributes. The establishment of the AI-RAN Alliance
[5] in February 2024 signals industry momentum toward GPU-accelerated AI-RAN integration,
though its high power consumption and terminal deployment limitations remain unresolved.

Technological Landscape and Innovation Gap

(1) Open-source projects like OAI/srsRAN rely on x86/CPUs, delivering subpar performance
without AI-native support;
(2) AI-RAN leverages GPUs for joint AI-RAN deployment but suffers from communication
energy efficiency far inferior to ASICs and cannot be deployed on end-user devices;
(3) Modem + NPU discrete solutions incur high inter-chip latency and lack flexibility for
academic research. This closed ecosystem creates two critical fractures: cutting-edge
academic research struggles to translate into industrial solutions, while operators remain
vendor-locked with diminished architectural flexibility.

These challenges underscore the imperative for an open-source, fully software-defined
baseband architecture with deep AI-communication convergence—the core mission of the
Venus initiative.

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