EdgeThought

Skymizer EdgeThought™: Ushering in a New Era of On-Device Generative AI As artificial intelligence continues its global proliferation, Skymizer leverages its profound expertise in compiler technology to introduce a revolutionary product: EdgeThought™. This compiler-centric Language Processing Unit (LPU) IP is engineered specifically for efficient on-device Large Language Model (LLM) inference. Its mission is to bring the power of generative AI to any edge device, making "AI on-device" a tangible reality. The core advantages of EdgeThought™ are encapsulated in its four foundational pillars: 1. Compact and Resource-Efficient Through an innovative compiler-driven, software-hardware co-design, EdgeThought™ features a single-core, resource-efficient architecture that minimizes the hardware demands for running large-scale language models. Engineered for minimal memory usage, it incorporates a dynamic decompression engine to process model weights on-the-fly. This significantly lowers storage costs and memory bandwidth usage while maintaining high inference precision, making it ideal for resource-constrained devices. 2. Accelerated and Premium Performance As a dedicated accelerator, EdgeThought™ is engineered to maximize inference performance on cost-effective silicon. It achieves this through optimal memory bandwidth utilization and superior hardware efficiency. Capitalizing on a decade of compiler expertise, its design maximizes the utilization of MACs (Multiply-Accumulate Units), ensuring accelerated, ultra-fast response times. This proven capability allows for the widespread deployment of LLM functionalities on mass-market devices without requiring expensive, leading-edge hardware. 3. Robust and Reliable Architecture EdgeThought™ is built on a strong and flexible architecture that performs reliably across a wide spectrum of applications, from low-power IoT devices to high-performance AI PCs and Edge Servers. The architecture is designed to handle diverse workloads with stability, supporting multi-user and multi-batch inference to meet the demands of more complex edge applications. 4. Integrated and a Seamless Ecosystem Incorporating the Language Instruction Set Architecture (LISA v2 & v3), EdgeThought™ provides a cohesive foundation that integrates seamlessly into existing ecosystems. It offers broad support for popular LLM frameworks, including HuggingFace Transformers, Nvidia Triton Inference Server, the OpenAI API, and LangChain. Furthermore, it is compatible with powerful toolkits for fine-tuning and Retrieval-Augmented Generation (RAG), such as HuggingFace PEFT, QLoRA, LlamaIndex, and LangChain, empowering developers with a comprehensive and accessible platform.
+ Inquiry