3 citations · 6 across the 4 of their papers we have counts for
5 papers
ShiftQAT: Quantization-Aware Training for the Numerical Contract of a Fixed-Function Edge CNN Accelerator
Oguzhan Buyuksolak, Osman Erman Okman, Mehmet Gorkem Ulkar
Fixed-function CNN accelerators make edge inference fast and energy-efficient, but they execute only networks that obey a precise numerical contract. On the MAX7800x family, this c…
EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting
Oguzhan Buyuksolak, Alican Gok, Osman Erman Okman
We introduce an efficient few-shot keyword spotting model for edge devices, EdgeSpot, that pairs an optimized version of a BC-ResNet-based acoustic backbone with a trainable Per-Ch…
Enhancing Few-shot Keyword Spotting Performance through Pre-Trained Self-supervised Speech Models
Alican Gok, Oguzhan Buyuksolak, Osman Erman Okman +1
Keyword Spotting plays a critical role in enabling hands-free interaction for battery-powered edge devices. Few-Shot Keyword Spotting (FS-KWS) addresses the scalability and adaptab…
L^3U-net: Low-Latency Lightweight U-net Based Image Segmentation Model for Parallel CNN Processors
Osman Erman Okman, Mehmet Gorkem Ulkar, Gulnur Selda Uyanik
In this research, we propose a tiny image segmentation model, L^3U-net, that works on low-resource edge devices in real-time. We introduce a data folding technique that reduces inf…
Ultra-Low Power Keyword Spotting at the Edge
Mehmet Gorkem Ulkar, Osman Erman Okman
Keyword spotting (KWS) has become an indispensable part of many intelligent devices surrounding us, as audio is one of the most efficient ways of interacting with these devices. Th…