Beyond Market Intelligence/inference engine

inference engine

inference engine on Beyond Market Intelligence: a running collection of 4 stories we have gathered and hand-picked because they are worth your time. Every post here touches on inference engine in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around inference engine, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.

Machine Learning

Language Models Can Control Their Own Attention [R]

Recent research introduces Declarative Attention (DA), a transformative approach to how language models manage context. Current models inefficiently scan vast amounts of data, even for focused queries. DA empowers models to *declare* where they need to attend—globally, locally, or to a specific region—significantly reducing the tokens processed during inference. Experiments with models like Gemma and Qwen show substantial reductions in attended tokens (up to 52.0%) with minimal accuracy impact.

I implemented a very tiny image generation model (latent flow transformer) on a RP2350 microcontroller - it can generate 128x128 images of faces [P]
Machine Learning

I implemented a very tiny image generation model (latent flow transformer) on a RP2350 microcontroller - it can generate 128x128 images of faces [P]

Astonishingly, a compact latent flow transformer model—ranging from 2.4 to 4 million parameters and quantized to int8—can now generate 128x128 face images directly on an RP2350 microcontroller in approximately 20 seconds. Utilizing AdaLN-Zero conditioning and CFG guidance, this innovative implementation streams weights via DMA from flash, leveraging ReLU² activation for increased sparsity and computational efficiency.

FreeToken Unlocks Frontier MoE Inference on Consumer Hardware via Dynamic Co-Execution
InfoQ

FreeToken Unlocks Frontier MoE Inference on Consumer Hardware via Dynamic Co-Execution

FreeToken, a new open-source inference engine developed by researchers at UC Berkeley and MIT, significantly expands the accessibility of Mixture-of-Experts (MoE) models. This innovative system enables faster, more efficient AI inference directly on consumer hardware through dynamic co-execution. FreeToken’s optimized scheduling and weight management unlock powerful edge AI applications and pave the way for self-hosted reasoning systems. For those seeking a deeper understanding of optimizing LLMs, explore our related article, "Quantization and Pruning Methods to Make Your LLM Leaner.”

I implemented the YOLO26n model inference from scratch using ARM64 Assembly Language (No framework) [P]
Machine Learning

I implemented the YOLO26n model inference from scratch using ARM64 Assembly Language (No framework) [P]

This impressive Bachelor's Final Project delivers a complete, from-scratch YOLO26n inference engine built using ARM64 Assembly Language and C—no frameworks required. The implementation prioritizes edge AI execution on Raspberry Pi 4, incorporating critical optimizations like ARM NEON SIMD, Winograd convolution, and cache-aware tiling. While performance gains didn't fully meet initial expectations, this project offers valuable insights into low-level neural network acceleration. For further exploration of efficient data encoding, consider “Ink & Switch Introduces Bijou64.” Repository: https://github.com/mohammad-ghaderi/YOLO26