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DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
PEZY-SC3: A MIMD Many-core Processor for Energy-efficient...
2022-12-19 · via cs.DC updates on arXiv.org

PEZY-SC3 is a highly energy- and area-efficient processor for supercomputers developed using TSMC 7nm process technology. It is the third generation of the PEZY-SCx series developed by PEZY Computing, K.K. Supercomputers equipped with the PEZY-SCx series have been deployed at several research centers and are used for large scale scientific calculations. PEZY-SC3 outperforms previous PEZY-SCx and other processors in terms of energy and area efficiency. To achieve high efficiency, PEZY-SC3 employs a MIMD many-core, fine-grained multithreading, and non-coherent cache, focusing on applications involving high thread-level parallelism. Our MIMD many-core-based architecture achieves high efficiency while providing higher programmability than existing architectures based on specialized tensor units with limited functionality or wide-SIMD. Another key point of this architecture is to achieve both high efficiency and high throughput without using complex and expensive units such as out-of-order schedulers. Moreover, our novel non-coherent and hierarchical cache system enables high scalability on many-core without compromising programmability. The energy efficiency of a system equipped with PEZY-SC3 is approximately 24.6 GFlops/W, and it ranked 12th in the Green500 (November 2021), which measures the energy efficiency of supercomputers. In terms of processor architecture, all the systems ranked higher than the PEZY-SC3 system are equipped with NVIDIA A100 or Preferred Networks MN-Core, and thus PEZY-SC3 is the third-ranked processor after them. While A100 and MN-Core achieve high energy efficiency with tensor units specialized for specific functions, PEZY-SC3 does not have such specialized tensor units and thus has higher programmability.