Zongwei Zhu

Boyu Li, Zongwei Zhu, Yi Xiong et al.

Large Language Models (LLMs) impose massive computational demands, driving the need for scalable multi-chiplet accelerators. However, existing mapping space exploration efforts for such accelerators primarily focus on traditional CNN/Transformer workloads and fail to adequately support the dynamic behaviors of mixed request types and variable sequence lengths in real-world LLM inference serving. To bridge this gap, we first propose a computation execution graph-based mapping encoding scheme that decouples micro-batches and layers, enabling fine-grained execution control on heterogeneous chiplets and flexibly representing various parallelism strategies. Second, building upon this scheme, we develop the Compass framework, which integrates an evaluation engine and a genetic algorithm-based mapping generation engine to achieve efficient mapping search. Compared to state-of-the-art works, our solution achieves an average EDP reduction of 63.12%.

Jing Cao, Zirui Lian, Weihong Liu et al.

Federated learning (FL) supports training models on geographically distributed devices. However, traditional FL systems adopt a centralized synchronous strategy, putting high communication pressure and model generalization challenge. Existing optimizations on FL either fail to speedup training on heterogeneous devices or suffer from poor communication efficiency. In this paper, we propose HADFL, a framework that supports decentralized asynchronous training on heterogeneous devices. The devices train model locally with heterogeneity-aware local steps using local data. In each aggregation cycle, they are selected based on probability to perform model synchronization and aggregation. Compared with the traditional FL system, HADFL can relieve the central server's communication pressure, efficiently utilize heterogeneous computing power, and can achieve a maximum speedup of 3.15x than decentralized-FedAvg and 4.68x than Pytorch distributed training scheme, respectively, with almost no loss of convergence accuracy.