GDDR7: The Ideal Memory Solution in AI Inference
The generative AI market is experiencing rapid growth, driven by the increasing parameter size of Large Language Models (LLMs). This growth is pushing the boundaries of performance requirements for training hardware within data centers. For an in-depth look at this, consider the insights provided in "HBM3E: All About Bandwidth". Once trained, these models are deployed across a diverse range of applications. They are transforming sectors such as finance, meteorology, image and voice recognition, healthcare, augmented reality, high-speed trading, and industrial, to name just a few.
The critical process that utilizes these trained models is called AI inference. Inference is the capability of processing real-time data through a trained model to swiftly and effectively generate predictions that yield actionable outcomes. While the AI market has primarily focused on the requirements of training infrastructure, there is an anticipated shift towards prioritizing inference as these models are deployed.
To read the full article, click here
Related Semiconductor IP
- GDDR7 PHY & Controller
- GDDR7 Memory Controller
- GDDR7 Synthesizable Transactor
- GDDR7 Memory Model
- GDDR7 DFI Verification IP
Related Blogs
- SiFive Accelerates RISC-V Vector Integration in XNNPACK for Optimized AI Inference
- How Does Crocodile Dundee Relate to AI Inference?
- Five Architectural Reasons Why FPGAs Are the Ultimate AI Inference Engines
- AImotive Expands Into Silicon IP for Deep Learning Inference Acceleration
Latest Blogs
- ReRAM in Automotive SoCs: When Every Nanosecond Counts
- AndeSentry – Andes’ Security Platform
- Formally verifying AVX2 rejection sampling for ML-KEM
- Integrating PQC into StrongSwan: ML-KEM integration for IPsec/IKEv2
- Breaking the Bandwidth Barrier: Enabling Celestial AI’s Photonic Fabric™ with Custom ESD IP on TSMC’s 5nm Platform