The memory wall refers to the physical barriers limiting how fast data can be moved in and out of memory. It’s a fundamental limitation with traditional architectures. In-memory computing or IMC addresses this challenge by running AI matrix calculations directly in the memory module, avoiding the overhead of sending data across the memory bus.
One of the hottest trends in artificial intelligence (AI) this year has been the emergence of popular generative AI models. With technologies including DALL-E and Stable Diffusion, there are a growing number of startups and use cases that are emerging.
Chiplet packaging is catching on with companies designing high-performance processors for data center and AI applications. While familiar names such as Intel and AMD are in this space, so are some smaller startup companies. One of them is d-Matrix, a young company developing technology for AI-compute and inference processors.
One of the hottest trends in artificial intelligence (AI) this year has been the emergence of popular generative AI models. With technologies including DALL-E and Stable Diffusion, there are a growing number of startups and use cases that are emerging.
Chiplet packaging is catching on with companies designing high-performance processors for data center and AI applications. While familiar names such as Intel and AMD are in this space, so are some smaller startup companies. One of them is d-Matrix, a young company developing technology for AI-compute and inference processors.
The memory wall refers to the physical barriers limiting how fast data can be moved in and out of memory. It’s a fundamental limitation with traditional architectures. In-memory computing or IMC addresses this challenge by running AI matrix calculations directly in the memory module, avoiding the overhead of sending data across the memory bus.
Hardware startup d-Matrix says the $44 million it raised in a Series A round today will help it continue development of a novel “chiplet” architecture that uses 6 nanometer chip embedded in SRAM memory modules for accelerating AI workloads.
The memory wall refers to the physical barriers limiting how fast data can be moved in and out of memory. It’s a fundamental limitation with traditional architectures. In-memory computing or IMC addresses this challenge by running AI matrix calculations directly in the memory module, avoiding the overhead of sending data across the memory bus.
