SEMRON develops a 3D-scalable AI inference chip using its proprietary CapRAM™ technology, which integrates compute-in-memory architecture to enhance energy efficiency and parameter density for AI applications. This technology addresses the high costs and power consumption of traditional AI chips, enabling efficient deployment of generative AI models directly on edge devices like smartphones and wearables.
Funding
$9.7M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
JCFounders
Product
Problem
Existing AI chips face rising costs and efficiency challenges as AI model complexity grows. Server-class chips are expensive and power-hungry, while mobile chips lack the performance to run generative AI models directly on edge devices. This limitation forces reliance on cloud computing, increasing latency and reducing margins for edge device manufacturers.
Solution
SEMRON addresses these challenges with CapRAM™, a 3D-scalable, compute-in-memory (CIM) technology that enhances energy efficiency and parameter density for AI inference. CapRAM™ utilizes a memcapacitive approach, achieving up to 50x greater energy efficiency compared to memristive solutions. By integrating memory and processing within the same device, CapRAM™ reduces data transfer bottlenecks and enables efficient deployment of generative AI models on devices like smartphones, wearables, and headsets. SEMRON provides a workflow for deploying AI models on its hardware, starting with Hugging Face or custom ONNX models, which are then compiled and packaged into a container to run directly on SEMRON hardware.
Target Audience
The primary target audience includes manufacturers of smartphones, wearables, headsets, and other edge devices seeking to integrate generative AI capabilities directly into their products.
Features
- 3D-scalable CapRAM™ architecture for high parameter density and energy efficiency
- Memcapacitive compute-in-memory technology, offering up to 50x greater energy efficiency than memristive solutions
- Achieves 0.2-1 TOPS/mW energy efficiency with multi-bit precision (INT8)
- Parameter density of 500M parameters/mm²
- Compiler converts floating-point models into efficient integer versions using a Brevitas-inspired API
- Embedded control software manages execution
- SEMRON Host Library ensures seamless integration with the customer’s hardware and software