Edge Computing

In the race to conquer time-series classification, most modern models have sprinted toward deeper Transformers and wider convolutional architectures. But what if the real breakthrough came not from complexity—but from symmetry? Enter PRISM (Per-channel Resolution-Informed Symmetric Module), a model that merges classical signal processing wisdom with deep learning, and in doing so, delivers a stunning blow to overparameterized AI. PRISM’s central idea is refreshingly simple: instead of building a massive model to learn everything from scratch, start by decomposing the signal like a physicist would—using symmetric FIR filters at multiple temporal resolutions, applied independently per channel. Like a prism splitting light into distinct wavelengths, PRISM separates time-series data into spectral components that are clean, diverse, and informative. ...

If you’ve ever tried to run a powerful AI model on a modest device—say, a drone, a farm robot, or even a Raspberry Pi—you’ve likely hit the wall of hardware limitations. Today’s most accurate models are big, bloated, and brittle when it comes to efficiency. Enter knowledge grafting, a refreshingly biological metaphor for a novel compression technique that doesn’t just trim the fat—it transfers the muscle. Rethinking Compression: Not What to Cut, But What to Keep Traditional model optimization methods—quantization, pruning, and distillation—all try to make the best of a difficult trade-off: shrinking the model while limiting the damage to performance. These methods often fall short, especially when you push compression past 5–6x. ...