Sotabase

Career

· Founder & CEO, Stealth Startup2025–
· Founding Engineer, Head of Platform, Infrastructure & Endpoints Engineering, Anyscale2019–2024
· Artificial Intelligence Research Intern, Intel Labs2019–
· PhD Student, University of California, Berkeley2018–2020
· Graduate Student (M.Sc.), Israel Institute of Technology, Technion2016–2018
· Chip Designer, Nvidia (Previously Mellanox Technologies)2015–2016
· Undergraduate Student (B.Sc.), Israel Institute of Technology, Technion2013–2017

Publications (0)

Ansor : Generating High-Performance Tensor Programs for Deep Learning
USENIX Symposium on Operating Systems Design and Implementation · 2020
503
cited
Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration
Design Automation Conference · 2019
307
cited
AutoCkt: Deep Reinforcement Learning of Analog Circuit Designs
Design, Automation and Test in Europe · 2020
182
cited
Machine Learning in Compiler Optimization
2020
133
cited
NeuroVectorizer: end-to-end vectorization with deep reinforcement learning
IEEE/ACM International Symposium on Code Generation and Optimization · 2019
113
cited
SIMPLER MAGIC: Synthesis and Mapping of In-Memory Logic Executed in a Single Row to Improve Throughput
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · 2020
94
cited
AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning
Conference on Machine Learning and Systems · 2020
80
cited
Gemmini: An Agile Systolic Array Generator Enabling Systematic Evaluations of Deep-Learning Architectures
arXiv.org · 2019
73
cited
TenSet: A Large-scale Program Performance Dataset for Learned Tensor Compilers
NeurIPS Datasets and Benchmarks · 2021
62
cited
IMAGING: In-Memory AlGorithms for Image processiNG
IEEE Transactions on Circuits and Systems Part 1: Regular Papers · 2018
55
cited
AutoPhase: Compiler Phase-Ordering for HLS with Deep Reinforcement Learning
IEEE Symposium on Field-Programmable Custom Computing Machines · 2019
44
cited
mMPU—A Real Processing-in-Memory Architecture to Combat the von Neumann Bottleneck
Applications of Emerging Memory Technology · 2019
34
cited
A View on Deep Reinforcement Learning in System Optimization
2019
33
cited
ProTuner: Tuning Programs with Monte Carlo Tree Search
arXiv.org · 2020
29
cited
RLDRM: Closed Loop Dynamic Cache Allocation with Deep Reinforcement Learning for Network Function Virtualization
IEEE Conference on Network Softwarization · 2020
14
cited
Supporting the Momentum Training Algorithm Using a Memristor-Based Synapse
IEEE Transactions on Circuits and Systems Part 1: Regular Papers · 2019
13
cited
Deep Reinforcement Learning in System Optimization
arXiv.org · 2019
8
cited
Learning to Vectorize Using Deep Reinforcement Learning
2019
4
cited
Memristor-based in-memory logic and its application in image processing
2020
2
cited
GitHub Version 0.0.1
2020
Sotabase