47 citations · 159 across the 11 of their papers we have counts for
11 papers
Deferred Objects to Enhance Smart Contract Programming with Optimistic Parallel Execution
George Mitenkov, Igor Kabiljo, Zekun Li +6
One of the main bottlenecks of blockchains is smart contract execution. To increase throughput, modern blockchains try to execute transactions in parallel. Unfortunately, however,…
LoopTune: Optimizing Tensor Computations with Reinforcement Learning
Dejan Grubisic, Bram Wasti, Chris Cummins +2
Advanced compiler technology is crucial for enabling machine learning applications to run on novel hardware, but traditional compilers fail to deliver performance, popular auto-tun…
LoopStack: a Lightweight Tensor Algebra Compiler Stack
Bram Wasti, José Pablo Cambronero, Benoit Steiner +2
We present LoopStack, a domain specific compiler stack for tensor operations, composed of a frontend, LoopTool, and an efficient optimizing code generator, LoopNest. This stack ena…
Large-scale image segmentation based on distributed clustering algorithms
Ran Lu, Aleksandar Zlateski, H. Sebastian Seung
Many approaches to 3D image segmentation are based on hierarchical clustering of supervoxels into image regions. Here we describe a distributed algorithm capable of handling a trem…
L3 Fusion: Fast Transformed Convolutions on CPUs
Rati Gelashvili, Nir Shavit, Aleksandar Zlateski
Fast convolutions via transforms, either Winograd or FFT, had emerged as a preferred way of performing the computation of convolutional layers, as it greatly reduces the number of…
PZnet: Efficient 3D ConvNet Inference on Manycore CPUs
Sergiy Popovych, Davit Buniatyan, Aleksandar Zlateski +2
Convolutional nets have been shown to achieve state-of-the-art accuracy in many biomedical image analysis tasks. Many tasks within biomedical analysis domain involve analyzing volu…
FFT Convolutions are Faster than Winograd on Modern CPUs, Here is Why
Aleksandar Zlateski, Zhen Jia, Kai Li +1
Winograd-based convolution has quickly gained traction as a preferred approach to implement convolutional neural networks (ConvNet) on various hardware platforms because it require…
ZNNi - Maximizing the Inference Throughput of 3D Convolutional Networks on Multi-Core CPUs and GPUs
Aleksandar Zlateski, Kisuk Lee, H. Sebastian Seung
Sliding window convolutional networks (ConvNets) have become a popular approach to computer vision problems such as image segmentation, and object detection and localization. Here…
ZNN - A Fast and Scalable Algorithm for Training 3D Convolutional Networks on Multi-Core and Many-Core Shared Memory Machines
Aleksandar Zlateski, Kisuk Lee, H. Sebastian Seung
Convolutional networks (ConvNets) have become a popular approach to computer vision. It is important to accelerate ConvNet training, which is computationally costly. We propose a n…
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection
Kisuk Lee, Aleksandar Zlateski, Ashwin Vishwanathan +1
Efforts to automate the reconstruction of neural circuits from 3D electron microscopic (EM) brain images are critical for the field of connectomics. An important computation for re…
Image Segmentation by Size-Dependent Single Linkage Clustering of a Watershed Basin Graph
Aleksandar Zlateski, H. Sebastian Seung
We present a method for hierarchical image segmentation that defines a disaffinity graph on the image, over-segments it into watershed basins, defines a new graph on the basins, an…