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With the increasing importance of deep learning workloads, many hardware accelerators have been proposed in both academia and industry. However, software tooling for the vast majority of them does not exist compared to the software ecosystem and innovations proposed for established platforms such as CPUs and GPUs. We observed that the lack of well-de!ned hardware-software interfaces and correctness testing tools like fast and scalable test oracles (also known as functional simulators) act as significant barriers to adopting these emerging accelerators in the software community. These interfaces and tools are essential in building software such as retargetable compilers and optimized kernels. To bridge these gaps, we first present TAIDL, an instruction specification language that provides novel constructs to describe the instruction set architectures (ISAs) of tensor accelerators. Next, given ISA definitions in TAIDL, we introduce techniques to automatically generate fast and scalable test oracles for diverse sets of accelerators, which are needed for testing software correctness of code that targets pre-silicon hardware designs. Automated generation of such tools reduces the burden on hardware architects and the repeated development efforts required across different accelerator platforms. Further, our techniques allow us to execute these simulators on GPUs, leading to highly scalable simulations. To demonstrate the expressivity of TAIDL, we instantiated several tensor accelerator ISAs with different compute capabilities and memory hierarchies. Further, we show that test oracles generated using TAIDL definitions are orders of magnitude faster and more scalable than existing instruction-level functional simulators, making them suitable for integration into software development cycles. TAIDL is available at https://github.com/act-compiler/taidl. 

https://proceedings.neurips.cc/paper_files/paper/2024/hash/06477eb61ea6b85c6608d42a222462df-Abstract-Datasets_and_Benchmarks_Track.html 

Fairness-awareness has emerged as an essential building block for the responsible use of artificial intelligence in real applications. In many cases, inequity in performance is due to the change in distribution over different regions. While techniques have been developed to improve the transferability of fairness, a solution to the problem is not always feasible with no samples from the new regions, which is a bottleneck for pure data-driven attempts. Fortunately, physics-based mechanistic models have been studied for many problems with major social impacts. We propose SimFair, a physics-guided fairness-aware learning framework, which bridges the data limitation by integrating physical-rule-based simulation and inverse modeling into the training design. Using temperature prediction as an example, we demonstrate the effectiveness of the proposed SimFair in fairness preservation. 

Solar power is a critical source of renewable energy, offering significant potential to lower greenhouse gas emissions and mitigate climate change. However, the cloud induced-variability of solar radiation reaching the earth’s surface presents a challenge for integrating solar power into the grid (e.g., storage and backup management). The new generation of geostationary satellites such as GOES-16 has become an important data source for large-scale and high temporal frequency solar radiation forecasting. However, no machine-learning-ready dataset has integrated geostationary satellite data with fine-grained solar radiation information to support forecasting model development and benchmarking with consistent metrics. We present SolarCube, a new ML-ready benchmark dataset for solar radiation forecasting. SolarCube covers 19 study areas distributed over multiple continents: North America, South America, Asia, and Oceania. The dataset supports short (i.e., 30 minutes to 6 hours) and long-term (i.e., day-ahead or longer) solar radiation forecasting at both point-level (i.e., specific locations of monitoring stations) and area-level, by processing and integrating data from multiple sources, including geostationary satellite images, physics-derived solar radiation, and ground station observations from different monitoring networks over the globe. We also evaluated a set of forecasting models for point- and image-based time-series data to develop performance benchmarks under different testing scenarios. The dataset is available at https://doi.org/10.5281/zenodo.11498739. A Python library is available to conveniently generate different variations of the dataset based on user needs, along with baseline models at https://github.com/Ruohan-Li/SolarCube.