3 papers
cs.DC2026
GPU Goldmines: Tunable WebGPU Kernels for Edge LLM Inference
Rithik Sharma, Reese Levine, Zheyuan Chen +3
As demand for LLM inference grows and consumer GPUs grow more capable, edge deployment is becoming increasingly attractive. WebGPU is compelling in this setting because it offers c…
cs.DC2026
Llamas on the Web: Memory-Efficient, Performance-Portable, and Multi-Precision LLM Inference with WebGPU
Reese Levine, Rithik Sharma, Nikhil Jain +5
Running language models in the browser presents a unique opportunity to build efficient, private, and portable AI applications, but requires contending with constrained memory avai…
cs.CR2026
Memory DisOrder: Memory Re-orderings as a Timerless Side-channel
Sean Siddens, Sanya Srivastava, Reese Levine +2
To improve efficiency, nearly all parallel processing units (CPUs and GPUs) implement relaxed memory models in which memory operations may be re-ordered, i.e., executed out-of-orde…