6 papers
Scalable Attention for 5G NR Channel Estimation
Mahdi Abdollahpour, Marco Bertuletti, Yichao Zhang +2
Attention-based neural estimators achieve strong channel-estimation accuracy, but the computational cost of global attention over the time-frequency resource grid grows quadratical…
TensorPool: A 3D-Stacked 8.4TFLOPS/4.3W Many-Core Domain-Specific Processor for AI-Native Radio Access Networks
Marco Bertuletti, Yichao Zhang, Diyou Shen +3
The upcoming integration of AI in the physical layer (PHY) of 6G radio access networks (RAN) will enable a higher quality of service in challenging transmission scenarios. However,…
TeraPool: A Physical Design Aware, 1024 RISC-V Cores Shared-L1-Memory Scaled-up Cluster Design with High Bandwidth Main Memory Link
Yichao Zhang, Marco Bertuletti, Chi Zhang +5
Shared L1-memory clusters of streamlined instruction processors (processing elements - PEs) are commonly used as building blocks in modern, massively parallel computing architectur…
A Compute&Memory Efficient Model-Driven Neural 5G Receiver for Edge AI-assisted RAN
Mahdi Abdollahpour, Marco Bertuletti, Yichao Zhang +3
Artificial intelligence approaches for base-band processing for radio receivers have demonstrated significant performance gains. Most of the proposed methods are characterized by h…
A 66-Gb/s/5.5-W RISC-V Many-Core Cluster for 5G+ Software-Defined Radio Uplinks
Marco Bertuletti, Yichao Zhang, Alessandro Vanelli-Coralli +1
Following the scale-up of new radio (NR) complexity in 5G and beyond, the physical layer's computing load on base stations is increasing under a strictly constrained latency and po…
Fast End-to-End Simulation and Exploration of Many-RISCV-Core Baseband Transceivers for Software-Defined Radio-Access Networks
Marco Bertuletti, Yichao Zhang, Mahdi Abdollahpour +2
The fast-rising demand for wireless bandwidth requires rapid evolution of high-performance baseband processing infrastructure. Programmable many-core processors for software-define…