The open chiplet ecosystem is gradually taking shape, driven by innovative design demonstrations. Alphawave Semi has announced the tape-out of what it claims to be the industry’s first off-the-shelf multi-protocol

Alphawave Semi proudly took the stage as a platinum sponsor at this year’s PCIe DevCon, showcasing the latest innovations in PCIe and reaffirming its leadership in high-performance connectivity solutions. The

Alphawave Semi, a global leader in high-speed connectivity and compute silicon, has introduced a new 9.2 Gbps HBM3E sub-system silicon platform. This platform, based on the company’s proven HBM3E IP,

Alphawave Semi, a global leader in high-speed connectivity and compute silicon, has announced the successful tape-out of the industry’s first off-the-shelf multi-protocol I/O connectivity chiplet on TSMC’s 7nm process. This

Alphawave Semi has broadened its partnership with Samsung Foundry, making advanced intellectual property (IP) for high-speed connectivity accessible to Samsung’s customers. This collaboration includes IP related to PCI Express 7.0,

Alphawave Semi is making significant strides in the semiconductor industry with its development of a compute chiplet based on the ARM Neoverse N3 Compute SubSystem (CSS). This marks a strategic

Alphawave Semi has teamed up with Arm to develop an advanced compute chiplet based on Arm Neoverse Compute Subsystems (CSS). This collaboration targets applications in artificial intelligence and machine learning

The chip industry is facing new challenges and opportunities in data management due to the increasing amount of data collected from design to manufacturing. The complexity of designs, customised and

Alphawave Semi is making a significant shift away from its traditional IP business by developing a compute chiplet using the ARM Neoverse N3 Compute SubSystem (CSS) for high-performance AI computing.

Artificial Intelligence (AI) is transforming various industries, including healthcare, finance, automotive, and manufacturing. The growing sophistication of AI applications, such as machine learning and neural networks, demands increasing computational power