5 papers
KerColle: Unlocking Fine-Grained GPU Concurrency in Vision-Language-Action Models
Anna Li, Christina Giannoula, Nandita Vijaykumar
Vision-Language-Action (VLA) models have emerged as foundational models for next-generation robotics. High VLA inference throughput is critical for meeting the control-rate require…
zkComposer: Decomposing Proof Construction to Scale zkML
Pawan Kumar Sanjaya, Christina Giannoula, Valdy Oktavian +4
Zero-knowledge machine learning (zkML) enables a server to perform verifiable inference while keeping model parameters private from the client. However, existing zkML systems incur…
DataGuard: Guaranteeing Private Training in Systolic-array Based Accelerators
Pawan Kumar Sanjaya, Christina Giannoula, Nikhil Shreekumar +6
Differential privacy (DP) and federated learning (FL) have emerged as important privacy-preserving approaches when using sensitive data to train machine learning (ML) models. FL en…
XPR: An Extensible Cross-Platform Point-Based Differentiable Renderer
Steve Rhyner, Sankeerth Durvasula, Aleksandr Kovalev +7
Point-based differentiable rendering underpins modern 3D reconstruction, novel-view synthesis, and learning-based graphics pipelines, but developing new rendering methods often req…
Low-Bitwidth Floating Point Quantization for Efficient High-Quality Diffusion Models
Cheng Chen, Christina Giannoula, Andreas Moshovos
Diffusion models are emerging models that generate images by iteratively denoising random Gaussian noise using deep neural networks. These models typically exhibit high computation…