6 papers
HeteroMosaic: Exposing and Exploiting Heterogeneous Execution Opportunities for Energy-Efficient Edge LLM Inference
Gregory Hyegang Jun, Wesley Pang, Eddie Richter +4
Modern edge system-on-chips (SoCs) combine CPUs, integrated GPUs (iGPUs), and neural processing units (NPUs), yet existing LLM runtimes typically make coarse device-level decisions…
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…
Learning Single-Image Super-Resolution in the JPEG Compressed Domain
Sruthi Srinivasan, Elham Shakibapour, Rajy Rawther +1
Deep learning models have grown increasingly complex, with input data sizes scaling accordingly. Despite substantial advances in specialized deep learning hardware, data loading co…
Combining Reinforcement Learning and Behavior Trees for NPCs in Video Games with AMD Schola
Tian Liu, Alex Cann, Ian Colbert +1
While the rapid advancements in the reinforcement learning (RL) research community have been remarkable, the adoption in commercial video games remains slow. In this paper, we outl…
Path Generation and Evaluation in Video Games: A Nonparametric Statistical Approach
Daniel Campa, Mehdi Saeedi, Ian Colbert +1
Navigation path traces play a crucial role in video game design, serving as a vital resource for both enhancing player engagement and fine-tuning non-playable character behavior. G…