Stanford CS348K, Spring 2022
VISUAL COMPUTING SYSTEMS

Visual computing tasks such as computational imaging, image/video understanding, and real-time 3D graphics are key responsibilities of modern computer systems ranging from sensor-rich smart phones, autonomous robots, and large datacenters. These workloads demand exceptional system efficiency and this course examines the key ideas, techniques, and challenges associated with the design of parallel, heterogeneous systems that accelerate visual computing applications. This course is intended for systems students interested in architecting efficient graphics, image processing, and computer vision platforms (both new hardware architectures and domain-optimized programming frameworks for these platforms) and for graphics, vision, and machine learning students that wish to understand throughput computing principles to design new algorithms that map efficiently to these machines.

Basic Info
Tues/Thurs 1:30-3:00pm
Location: 540-108
Instructor: Kayvon Fatahalian
Welcome to CS348K Spring 2022. Please see the course info page for more info on policies and logistics, and well as answers to common questions like "Am I prepared to take this class?" This course is a paper-reading and in-class discussion-based course, so live attendence is expected of all participants.
Spring 2022 Schedule
Mar 29
Review of multi-core, multi-threading, SIMD, caches, and the value of hardware specialization
Mar 31
Algorithms for taking raw sensor pixels to an RGB image: demosaicing, sharpening, correcting lens aberrations, multi-shot alignment/merging, image filtering
Apr 05
Multi-scale processing with Gaussian and Laplacian pyramids, HDR (local tone mapping), portrait mode
Apr 07
Autofocus, autoexposure, use of ML in advanced camera operations, the Frankencamera.
Apr 12
Balancing locality, parallelism, and work, fusion and tiling, design of the Halide domain-specific language, automatically scheduling image processing pipelines
Apr 14
Popular DNN trunks and topologies, where the compute lies in modern networks, data layout optimizations, scheduling decisions, modern code generation frameworks
Apr 19
GPUs, TPUs, special instructions for DNN evaluation (and their efficiency vs custom ASIC), choice of precision in arithmetic, modern commercial DNN accelerators, flexibility vs efficiency trade-offs
Apr 21
If the most important step of ML is acquiring labeled data for training and validation, why don't we have better systems for it?
Apr 26
Systems for specifying models at a higher level of abstraction than DNN architecture graphs (Overton, Ludwig). Goal: removing the need for a low-level ML engineer.
Apr 28
H.264 video representation/encoding, parallel encoding, motivations for ASIC acceleration, ML-based compression methods, emerging opportunities for compression when machines, not humans, will observe most images
May 03
System design issues for building a video conferencing system: reducing latency, bandwidth, etc. How real-time video analysis will enable richer video-based applications.
May 05
The light field, initial discussion of NeRF algorithms
May 10
discussion of the arc of NeRF papers + review of the 3D rasterization pipeline (so we can talk about performance challenges next.)
May 12
Scheduling graphics pipeline onto parallel GPUs, key optimizations for modern, power-optimized mobile GPUs.
May 17
Modern hardware acceleration (RTX GPUs), memory coherence challenges, converting noisy images to clean images using neural techniques.
May 19
Topic depends on how fast we get through other lectures.
May 24
Epic’s Nanite Renderer (Guest Lecture: Brian Karis - Epic Games)
Brian Karis will talk about the design of Epic's Nanite renderer.
May 26
Rendering and Simulation for Model Training
How might systems for rendering and simulating virtual worlds be architected differently to support the needs of training machines instead of playing video games? (a.k.a. rendering for machine eyes, not human eyes)
May 31
The Slang Shading Language (Guest Lecture: Yong He and Teresa Foley - NVIDIA)
The design and implementation of Slang, discussion about transferring academic systems research into industry efforts.
Jun 02
Guest Lecture II
To be announced
Assignments

In addition to expectation that all students attend and participate in discussions in live lecture, there will be two short programming assignments and a self-selected term project.

Apr 18 Burst Mode HDR Camera RAW Processing
Apr 29 Scheduling a DNN Conv Layer (Making Students Appreciate cuBLAS)
Jun 3 Term Project