This study introduces a memory-efficient mixed representation for deep learning recommendation models (DLRM), addressing the embedding table memory bottleneck from growing data scale. By distinguishing between frequently accessed (hot) and infrequently accessed (cold) embeddings, we store hot embeddings in a compact table while representing cold embeddings using a deep hash embedding (DHE) network, significantly reducing memory usage. This hybrid approach performs table lookups for hot embeddings and parallelized computations for cold embeddings, minimizing training time while maintaining accuracy. Experimental results demonstrate that our method outperforms other embedding reduction techniques in memory efficiency, accuracy, and training speed in CPU-GPU hybrid environments.

Transparency logs are designed to help users audit untrusted servers. For example, Certificate Transparency (CT) enables users to detect when a compromised Certificate Authority (CA) has issued a fake certificate. Practical state-of-the-art transparency log systems, however, suffer from high monitoring costs when used for low-latency applications. To reduce monitoring costs, such systems often require users to wait an hour or more for their updates to take effect, inhibiting low-latency applications. We propose Merkle2, a transparency log system that supports both efficient monitoring and low-latency updates. To achieve this goal, we construct a new multi-dimensional, authenticated data structure that nests two types of Merkle trees, hence the name of our system, Merkle2. Using this data structure, we then design a transparency log system with efficient monitoring and lookup protocols that enables low-latency updates. In particular, all the operations in Merkle2are independent of update intervals and are (poly)logarithmic to the number of entries in the log. Merkle2not only has excellent asymptotics when compared to prior work, but is also efficient in practice. Our evaluation shows that Merkle2propagates updates in as little as 1 second and can support 100× more users than state-of-the-art transparency logs.