TCDM Burst Access: Breaking the Bandwidth Barrier in Shared-L1 RVV Clusters Beyond 1000 FPUs
January 24, 2025 Β· Declared Dead Β· π Design, Automation and Test in Europe
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Diyou Shen, Yichao Zhang, Marco Bertuletti, Luca Benini
arXiv ID
2501.14370
Category
cs.AR: Hardware Architecture
Cross-listed
cs.DC
Citations
0
Venue
Design, Automation and Test in Europe
Last Checked
3 months ago
Abstract
As computing demand and memory footprint of deep learning applications accelerate, clusters of cores sharing local (L1) multi-banked memory are widely used as key building blocks in large-scale architectures. When the cluster's core count increases, a flat all-to-all interconnect between cores and L1 memory banks becomes a physical implementation bottleneck, and hierarchical network topologies are required. However, hierarchical, multi-level intra-cluster networks are subject to internal contention which may lead to significant performance degradation, especially for SIMD or vector cores, as their memory access is bursty. We present the TCDM Burst Access architecture, a software-transparent burst transaction support to improve bandwidth utilization in clusters with many vector cores tightly coupled to a multi-banked L1 data memory. In our solution, a Burst Manager dispatches burst requests to L1 memory banks, multiple 32b words from burst responses are retired in parallel on channels with parametric data-width. We validate our design on a RISC-V Vector (RVV) many-core cluster, evaluating the benefits on different core counts. With minimal logic area overhead (less than 8%), we improve the bandwidth of a 16-, a 256-, and a 1024--Floating Point Unit (FPU) baseline clusters, without Tightly Coupled Data Memory (TCDM) Burst Access, by 118%, 226%, and 77% respectively. Reaching up to 80% of the cores-memory peak bandwidth, our design demonstrates ultra-high bandwidth utilization and enables efficient performance scaling. Implemented in 12-nm FinFET technology node, compared to the serialized access baseline, our solution achieves up to 1.9x energy efficiency and 2.76x performance in real-world kernel benchmarkings.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Hardware Architecture
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Corona: System Implications of Emerging Nanophotonic Technology
R.I.P.
π»
Ghosted
A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)
R.I.P.
π»
Ghosted
SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning
R.I.P.
π»
Ghosted
Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks
R.I.P.
π»
Ghosted
SpArch: Efficient Architecture for Sparse Matrix Multiplication
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted
Explanation in Artificial Intelligence: Insights from the Social Sciences
R.I.P.
π»
Ghosted