Deep neural networks (DNNs) dominate workloads on cloud and edge platforms. Meanwhile, the hardware platform towards the heterogeneous system with various accelerators. By mapping layers to their different preferred accelerators, the computation cost of each layer can be reduced. While mapping these layers on the same accelerator can reduce the inter-accelerator communication cost. These two costs are often competing and difficult to optimize simultaneously. Therefore, the core challenge in achieving efficient execution of DNN workloads on heterogeneous systems is: how to map layers to achieve the best trade-off between computation and communication costs. Existing works group layers into blocks and perform blockwise mapping to reduce inter-layer communication within blocks. However, when grouping layers, they typically rely on modelagnostic rules, which fail to hide critical inter-layer communication within blocks for diverse DNNs. Moreover, after block mapping, the lack of intra-block resource allocation further increases computation cost of block. In this paper, we proposeGHCoM, a novel block-wise mapping framework for exploring the effective cost trade-offs.GHCoMemploys an adaptive grouping strategy to guide layer grouping based on the topology of DNNs and dynamically adjust the grouping according to the trade-off target. Furthermore,GHCoMconsiders the fine-grained allocation of computation (i.e., processing elements) and communication (i.e., on-chip bandwidth) resources within each block to mitigate interlayer resource contention. To jointly optimize layer grouping, block-wise mapping and intra-block resource allocation,GHCoMleverages a two-level genetic algorithm (GA) with tailored encodings and operators that capture the interdependence across the entire design space. Experiments across various workloads and system configurations show thatGHCoMconsistently outperforms state-of-the-art baselines, achieving 1.08× to 4.79× speedup in execution latency and reducing energy consumption by 1.83% to 87.71%.

Generating explanations for deep neural networks (DNNs) can make them more trustworthy in real-world applications. For a text classification task, existing methods visualize the contributions of words or word interactions layer by layer in a traversal manner, to assist users in understanding the decision-making of models. However, all these methods only focus on the explanation performance while ignoring inefficiencies in explaining due to the traversal manner. This means that the explanation is not available to users in a timely manner, so users may no longer use it due to the big time cost. To overcome this problem, we propose HETSG, an interaction-based method for explaining text classification models quickly and faithfully, by a simple and effective two-step building strategy. Such a strategy captures the important interaction by first determining the important position and then confirming the direction of interaction, without iterating over all word interactions. We also provide a novel metric to accurately evaluate the performance of each interpretation method. The proposed method is compared with baseline methods (baselines) on six text classification datasets to explain three natural language processing (NLP) models. Experimental results show that our method outperforms all baselines with higher efficiency and it is also competitive in performance.