Software-Defined Stochastic Inference Engine (SDSIE) Initial public release and technical research specification for the Software-Defined Stochastic Inference Engine (SDSIE). Overview This specification outlines an adaptive virtual runtime architecture designed to execute generative AI workloads with high energy efficiency on conventional silicon by dynamically modulating precision, temporal execution, and memory states. Included Assets Full Research Whitepaper (PDF)
Obtaining high-quality annotated data has become a primary bottleneck for training deep learning models, particularly for dense prediction tasks like semantic segmentation and video salient object segmentation. The demand for meticulous, pixel-level labeling makes fully-supervise…
The global energy transition towards renewables and the proliferation of electrified transportation are driving an unprecedented demand for power conversion systems that achieve ultra-high power density, high reliability, and bidirectional power flow. Wide band gap (WBG) semicond…
Deep neural networks have achieved substantial success in image, text, and signal analysis, but their advantage is less consistent for heterogeneous tabular data, where tree-based ensemble methods often remain strong baselines. This study proposes CANON (Cross-Attention Neuro-sym…
Large language models frequently possess the knowledge needed to answer a question correctly yet commit to the wrong response. This paper presents friction-guided inference, a calibrated inference-time pipeline that uses the model's own logprob distribution — available at zero co…
CLIP is a proposed AI-native learning platform built around Digital Ink Intelligence. Rather than replacing handwriting with typing, CLIP captures every pen stroke as structured digital data in real time, derives thirteen multidimensional Learning Signals, generates comprehensive…