{"id":395,"date":"2026-08-07T10:38:20","date_gmt":"2026-08-07T14:38:20","guid":{"rendered":"https:\/\/carleton.ca\/share\/?p=395"},"modified":"2026-08-07T10:42:11","modified_gmt":"2026-08-07T14:42:11","slug":"thesis-defense-2","status":"publish","type":"post","link":"https:\/\/carleton.ca\/share\/2026\/thesis-defense-2\/","title":{"rendered":"Thesis Defence"},"content":{"rendered":"

Aaron Yu<\/strong><\/a>
\nDATE<\/strong>: Monday, August 17, 2026, 11:00, Location<\/strong>: online (Zoom link<\/a>)
\nTHESIS (MASc.) TITLE<\/strong>: Hardware-Optimized Spiking Neural Networks for High-Speed Event-Based Optical Flow Estimation<\/h4>\n

Abstract<\/strong>: This thesis presents EmFlow, a hardware-oriented spiking neural network (SNN) for high-speed embedded event-based optical flow estimation. EmFlow uses sparse convolutional processing, delayed upscaling, 1-bit spike feature maps, and limited persistent membrane state to reduce memory and computation. It is evaluated on HFlow320, a synthetic event-based human-motion dataset developed for this work, as well as MVSEC evaluation sequences and the DSEC optical-flow benchmark. With the baseline 25 ms hardware-oriented input window, EmFlow achieves 2.39 px endpoint error (EPE) and 35.02\u25e6 average angular error (AAE) on HFlow320, 1.77 px EPE and 28.01\u25e6 AAE on MVSEC, and 9.43 px EPE and 28.13\u25e6 AAE on DSEC. The design is implemented on an AMD Kria KV260 field-programmable gate array (FPGA) with a Prophesee GenX320 event camera. The EmFlow accelerator module uses 9.6k look-up tables (LUTs), 11.8k flip-flops (FFs), 16 digital signal processing (DSP) blocks, 20\/1 block RAM (BRAM) 36K\/18K tiles, and 12 UltraRAM (URAM) tiles while sustaining a 40 frames\/s (FPS) displayed output rate in live demonstrations. Event-to-observed-flow response measurements range from approximately 1.7 to 7 ms, while final displayed flow frames are accumulated over five 5 ms event windows. During operation, the system consumes 3.7 to 4.0 W total measured system-on-module (SOM) power, corresponding to approximately 0.1 to 0.4 W above the loaded idle baseline.<\/p>\n","protected":false},"excerpt":{"rendered":"

Aaron Yu DATE: Monday, August 17, 2026, 11:00, Location: online (Zoom link) THESIS (MASc.) TITLE: Hardware-Optimized Spiking Neural Networks for High-Speed Event-Based Optical Flow Estimation Abstract: This thesis presents EmFlow, a hardware-oriented spiking neural network (SNN) for high-speed embedded event-based optical flow estimation. EmFlow uses sparse convolutional processing, delayed upscaling, 1-bit spike feature maps, and […]<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_mi_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[25,1],"tags":[],"yoast_head":"\nThesis Defence - Specific-domain Hardware Research Laboratory<\/title>\n<meta name=\"description\" content=\"Aaron Yu DATE: Monday, August 17, 2026, 11:00, Location: online (Zoom link) THESIS (MASc.) 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