DIBS: Database Isolation By Scheduling

(Kevin Gaffney, Univ. Wisconsin-Madison, is presenting on Wed. 2/5/20 at 11:00AM & 7:00PM ET)

Preventing concurrent transactions from interfering with each other is a performance challenge in modern relational database systems. The majority of transaction isolation systems achieve isolation through either mid-execution or post-execution validation. As a result, transaction managers are often complex and require developing entire systems around them. We show that database isolation can be guaranteed by analyzing and scheduling declarative transactions outside of the database using pre-execution validation. We provide an implementation that does so with no knowledge of the database system’s implementation or state, achieving competitive performance on transaction processing benchmarks.

Deep Learning Acceleration with Neuron-to-Memory Transformation

Description:
(Yeseong Kim, UCSD, presenting at 11:00AM and 7:00PM Eastern Time on Wednesday, January 22, 2020)

Abstract: 

In this talk, I will discuss our framework for deep neural network (DNN) acceleration, called RAPIDNN, which performs neuron-to-memory transformation for a highly-parallel, memory-centric architecture. RAPIDNN reinterprets a DNN model and maps it into a specialized accelerator, which is designed using non-volatile memory blocks that model four fundamental DNN operations. Our evaluation shows that RAPIDNN achieves 49.5× energy efficiency improvement and 10.9× speedup as compared to PipeLayer, a state-of-the-art DNN accelerator while ensuring less than 0.5% quality loss.