Computing Industry Insights
Exploring Innovations and Challenges in the World of Computing
MulticoreWare’s gene pool consists of expertise in performance optimization for image and video processing pipelines, crafting software solutions and designing tools tailored for multi-core and heterogeneous computing environments.
We possess extensive expertise spanning various layers of AI/ML software stacks for multiple clients. This encompasses tasks such as kernel development, kernel optimization, and integrating ML framework support into their backend APIs. We collaborate with these clients to facilitate the deployment of their new platforms, leveraging our experience in DSP and embedded drivers.
Our solutions cover a wide range of areas, allowing for customization to meet the unique needs of various industries and multiple use cases such as:
Utilizing powerful computer systems and parallel processing techniques to tackle complex and computationally intensive tasks, we deliver significantly faster and more efficient results.
Create ML pipelines to enhance the efficiency of model training and deployment, including tools for organizing the management of ML models, implementing monitoring and alerting solutions.
Ensure the reliability, security, & scalability of cloud infrastructure, with expertise in deploying thorough testing approaches, that harness cutting-edge cloud computing technology and testing methodologies.
SOC & IP Platforms offerings include services in AI/ML Accelerators, X86 based Processors, ARM based Processors, RISC-V based Processors, GPUs, and Vision & Audio DSPs.
This includes Server computing, Client computing, Mobile computing, Cloud infra & Data Centres, that are used to support computing operations.
Our service offerings encompass the following:
VaLVe, an IP from MulticoreWare, is a powerful programmer productivity tool designed for SIMD programming. It extends support to multiple architectures, such as ARM V9 and RISC-V.
VaLVe is a two-layer library enabling explicit vector programming that makes vectors look and feel like native data types.
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