Wojciech Romaszkan

According to our database, Wojciech Romaszkan authored at least 10 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

SCIMITAR: Stochastic Computing In-Memory In-Situ Tracking ARchitecture for Event-Based Cameras.

Wojciech Romaszkan, Jiyue Yang, Alexander Graening, Vinod Kurian Jacob, Jishnu Sen, Sudhakar Pamarti, Puneet Gupta

IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., November, 2024

A 278-514M Event/s ADC-Less Stochastic Compute-In-Memory Convolution Accelerator for Event Camera.

Jiyue Yang, Alexander Graening, Wojciech Romaszkan, Vinod K. Jacob, Puneet Gupta, Sudhakar Pamarti

Proceedings of the IEEE Symposium on VLSI Technology and Circuits 2024, 2024

2023

REX-SC: Range-Extended Stochastic Computing Accumulation for Neural Network Acceleration.

Tianmu Li, Wojciech Romaszkan, Sudhakar Pamarti, Puneet Gupta

IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., December, 2023

Efficient Machine Learning Acceleration at the Edge

Wojciech Romaszkan

PhD thesis, 2023

Cost-Driven Hardware-Software Co-Optimization of Machine Learning Pipelines.

Ravit Sharma, Wojciech Romaszkan, Feiqian Zhu, Puneet Gupta, Ankur Mehta

CoRR, 2023

2022

SASCHA - Sparsity-Aware Stochastic Computing Hardware Architecture for Neural Network Acceleration.

Wojciech Romaszkan, Tianmu Li, Puneet Gupta

IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2022

2021

SWIS - Shared Weight bIt Sparsity for Efficient Neural Network Acceleration.

Shurui Li, Wojciech Romaszkan, Alexander Graening, Puneet Gupta

CoRR, 2021

GEO: Generation and Execution Optimized Stochastic Computing Accelerator for Neural Networks.

Tianmu Li, Wojciech Romaszkan, Sudhakar Pamarti, Puneet Gupta

Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2021

2020

3PXNet: Pruned-Permuted-Packed XNOR Networks for Edge Machine Learning.

Wojciech Romaszkan, Tianmu Li, Puneet Gupta

ACM Trans. Embed. Comput. Syst., 2020

ACOUSTIC: Accelerating Convolutional Neural Networks through Or-Unipolar Skipped Stochastic Computing.

Wojciech Romaszkan, Tianmu Li, Tristan Melton, Sudhakar Pamarti, Puneet Gupta

Proceedings of the 2020 Design, Automation & Test in Europe Conference & Exhibition, 2020