2 citations · 5 across the 7 of their papers we have counts for
7 papers
Phantora: Maximizing Code Reuse in Simulation-based Machine Learning System Performance Estimation
Jianxing Qin, Jingrong Chen, Xinhao Kong +8
Modern machine learning (ML) training workloads place substantial demands on both computational and communication resources. Consequently, accurate performance estimation has becom…
Accelerating Markov Random Field Inference with Uncertainty Quantification
Ramin Bashizade, Xiangyu Zhang, Sayan Mukherjee +1
Statistical machine learning has widespread application in various domains. These methods include probabilistic algorithms, such as Markov Chain Monte-Carlo (MCMC), which rely on g…
Lightweight Inter-transaction Caching with Precise Clocks and Dynamic Self-invalidation
Pulkit A. Misra, Srihari Radhakrishnan, Jeffrey S. Chase +2
Distributed, transactional storage systems scale by sharding data across servers. However, workload-induced hotspots result in contention, leading to higher abort rates and perform…
Beyond Application End-Point Results: Quantifying Statistical Robustness of MCMC Accelerators
Xiangyu Zhang, Ramin Bashizade, Yicheng Wang +3
Statistical machine learning often uses probabilistic algorithms, such as Markov Chain Monte Carlo (MCMC), to solve a wide range of problems. Probabilistic computations, often cons…
Multi-version Indexing in Flash-based Key-Value Stores
Pulkit A. Misra, Jeffrey S. Chase, Johannes Gehrke +1
Maintaining multiple versions of data is popular in key-value stores since it increases concurrency and improves performance. However, designing a multi-version key-value store ent…
A Case for Quantifying Statistical Robustness of Specialized Probabilistic AI Accelerators
Xiangyu Zhang, Sayan Mukherjee, Alvin R. Lebeck
Statistical machine learning often uses probabilistic algorithms, such as Markov Chain Monte Carlo (MCMC), to solve a wide range of problems. Many accelerators are proposed using s…
Combined Compute and Storage: Configurable Memristor Arrays to Accelerate Search
Yang Liu, Chris Dwyer, Alvin R. Lebeck
Emerging technologies present opportunities for system designers to meet the challenges presented by competing trends of big data analytics and limitations on CMOS scaling. Specifi…