Search for: All records

In-memory computing with large last-level caches is promising to dramatically alleviate data movement bottlenecks and expose massive bitline-level parallelization opportunities. However, key challenges from its unique execution model remain unsolved: automated parallelization, transparently orchestrating data transposition/alignment/broadcast for bit-serial logic, and mixing in-/near-memory computing. Most importantly, the solution should be programmer friendly and portable across platforms. Our key innovation is an execution model and intermediate representation (IR) that enables hybrid CPU-core, in-memory, and near-memory processing. Our IR is the tensor dataflow graph (tDFG), which is a unified representation of in-memory and near-memory computation. The tDFG exposes tensor-data structure information so that the hardware and runtime can automatically orchestrate data management for bitserial execution, including runtime data layout transformations. To enable microarchitecture portability, we use a two-phase, JIT-based compilation approach to dynamically lower the tDFG to in-memory commands. Our design, infinity stream, is evaluated on a cycle-accurate simulator. Across data-processing workloads with fp32, it achieves 2.6× speedup and 75% traffic reduction over a state-of-the-art near-memory computing technique, with 2.4× energy efficiency. 

We investigate the potential of using Global Navigation Satellite System (GNSS) observations to directly forecast full tsunami waveforms in real time. We train convolutional neural networks to use less than 9 min of GNSS data to forecast the full tsunami waveforms over 6 hr at select locations, and obtain accurate forecasts on a test data set. Our training and test data consists of synthetic earthquakes and associated GNSS data generated for the Cascadia Subduction Zone using the MudPy software, and corresponding tsunami waveforms in Puget Sound computed using GeoClaw. We use the same suite of synthetic earthquakes and waveforms as in earlier work where tsunami waveforms were used for forecasting, and provide a comparison. We also explore varying the number of GNSS stations, their locations, and their observation durations.