Network-on-Chip (NoC) accelerators with heterogeneous Processing-in-Memory (PIM) cores achieve superior performance than homogeneous ones for neural networks. Dedicated simulators and architecture search frameworks are pivotal for obtaining performance, power, and area (PPA) metrics, as well as guiding the design process. However, existing simulators are primarily designed for homogeneous NoC and lack support for simulating heterogeneous PIM-based NoC architectures. Besides, current search frameworks for heterogeneous NoC architectures only focus on workload allocation and mapping strategies, failing to explore heterogeneous PIM configurations in a larger design space. In this work, we propose HPIM-NoC, a joint simulation and search framework for heterogeneous PIM-based NoC architectures. HPIM-NoC not only supports the simulation of heterogeneous PIM cores, but also provides more accurate latency results by introducing NoC transmission delays and pipelines in co-simulation. HPIM-NoC implements a three-stage heterogeneous search process based on priori knowledge and employs a specific simulated annealing algorithm tailored for heterogeneous architecture search. The search process is accelerated by precomputing core PPA metrics and reducing NoC simulation frequency. In addition, the framework integrates a customized layout algorithm to optimize the placement of heterogeneous NoC, minimizing communication latency and overall area. Experimental results on various neural networks demonstrate that HPIM-NoC can quickly find near-optimal configurations within a limited time. The proposed acceleration method reduces the search time of HPIM-NoC by $2.12 \times$, $2.17 \times$, and $2.96 \times$, respectively. Compared to homogeneous architectures, the Fusions of Metrics (FoMs) of heterogeneous PIM-based NoC architectures found by HPIM-NoC are reduced by $\mathbf{1. 1 8 \%, ~} \mathbf{1 6. 9 4 \%}$, and $\mathbf{3 7. 4 1 \%}$ for ResNet-18 under three settings, respectively.

The emerging analog Processing-in-Memory (PIM) architectures have shown great potential to overcome the memory wall problem and accelerate neural network (NN) inference. However, different from digital architectures, the computation accuracy of analog PIM architectures is directly impacted by various errors, which are related to both software and hardware parameters. Existing PIM simulators mainly adopt the bit-and-crossbar slicing paradigm to evaluate the accuracy under various errors. Each MVM operation is performed bit by bit and crossbar by crossbar, which is extremely time-consuming, especially for models with a larger number of parameters, such as large language models (LLMs).In this work, we propose an error-injected robustness metric, unifying various errors into the weight dimension and facilitating joint error analysis. Based on the error-injected robustness metric, we propose a Non-Ideal PIM Accuracy (NIPA) evaluation model for relative accuracy evaluation, considering the coupling effect (i.e., various errors can be affected by the same factor) among various errors using NN’s prior information. We further propose a non-slicing absolute accuracy evaluation method, eliminating the need for the time-consuming bit-and-crossbar slicing process. Extensive experiments on CNNs and LLMs validate that the proposed NIPA evaluation model achieves high correlations of up to 0.91 with the absolute accuracy evaluated by DNN+NeuroSim. At the same time, compared to existing bit-and-crossbar slicing evaluation methods, the proposed non-slicing absolute accuracy evaluation method achieves up to 105.8× speedup with average evaluation errors as low as 0.29%.

We study a decentralized version of Moving Agents in Formation (MAiF), a variant of Multi-Agent Path Finding aiming to plan collision-free paths for multiple agents with the dual objectives of reaching their goals quickly while maintaining a desired formation. The agents must balance these objectives under conditions of partial observation and limited communication. The formation maintenance depends on the joint state of all agents, whose dimensionality increases exponentially with the number of agents, rendering the learning process intractable. Additionally, learning a single policy that can accommodate different linear preferences for these two objectives presents a significant challenge. In this paper, we propose Mean-Field Control with Envelop Q-learning (MFC-EQ), a scalable and adaptable learning framework for this bi-objective multi-agent problem. We approximate the dynamics of all agents using mean-field theory while learning a universal preference-agnostic policy through envelop Q-learning. Our empirical evaluation of MFC-EQ across numerous instances shows that it outperforms state-of-the-art centralized MAiF baselines. Furthermore, MFC-EQ effectively handles more complex scenarios where the desired formation changes dynamically—a challenge that existing MAiF planners cannot address.