Machine learning (ML) applications are ubiquitous. They run in different environments such as datacenters, the cloud, and even on edge devices. Despite where they run, distributing ML training seems the only way to attain scalable, high-quality learning. B ...
Analytical engines rely on in-memory caching to avoid disk accesses and provide timely responses by keeping the most frequently accessed data in memory. Purely frequency- & time-based caching decisions, however, are a proxy of the expected query execution ...
Advances in mobile computing have paved the way for new types of distributed applications that can be executed solely by mobile devices on Device-to-Device (D2D) ecosystems (e.g., crowdsensing). Sophisticated applications, like cryptocurrencies, need distr ...
We propose centralized brain observatories for large-scale recordings of neural activity in mice and non-human primates coupled with cloud-based data analysis and sharing. Such observatories will advance reproducible systems neuroscience and democratize ac ...
CNG power system (1) comprising a storage tank (6) connected fluidically to a fuel conversion system (2) via an energy transfer system (4), the fuel conversion system (2) comprising a power unit using CNG as fuel and generating gas emissions comprising C02 ...
Non-Volatile Memory (NVM) is an emerging type of memory device that provides fast, byte-addressable, and high-capacity durable storage. NVM sits on the memory bus and allows durable data structures designs similar to the in-memory equivalent ones. Expensiv ...
