Parsing the Mindboggling Cost of Ownership of Generative AI
By Lauro Rizzatti, VSORA
EETimes (November 2, 2023)
The latest algorithms, such as GPT-4, pose a challenge to the current state-of-the-art processing hardware, and GenAI accelerators aren’t keeping up. In fact, no hardware on the market today can run the full GPT-4.
Current large language model (LLM) development focuses on creating smaller but more specialized LLMs that can run on existing hardware is a diversion. The GenAI industry needs semiconductor innovations in computing methods and architectures capable of delivering performance of multiple petaFLOPS with efficiency greater than 50%, reducing latency to less than two second per query, constraining energy consumption and shrinking cost to 0.2 cent per query.
Once this is in place–and it is only matter of time–the promise of transformers when deployed on edge devices will be fully exploited.
To read the full article, click here
Related Semiconductor IP
- USB 20Gbps Device Controller
- AGILEX 7 R-Tile Gen5 NVMe Host IP
- 100G PAM4 Serdes PHY - 14nm
- Bluetooth Low Energy Subsystem IP
- Multi-core capable 64-bit RISC-V CPU with vector extensions
Related White Papers
- MIPI in next generation of AI IoT devices at the edge
- Revolutionizing Consumer Electronics with the power of AI Integration
- The benefit of non-volatile memory (NVM) for edge AI
- Revolutionizing AI Inference: Unveiling the Future of Neural Processing
Latest White Papers
- CRADLE: Conversational RTL Design Space Exploration with LLM-based Multi-Agent Systems
- On the Thermal Vulnerability of 3D-Stacked High-Bandwidth Memory Architectures
- OmniSim: Simulating Hardware with C Speed and RTL Accuracy for High-Level Synthesis Designs
- Balancing Power and Performance With Task Dependencies in Multi-Core Systems
- LLM Inference with Codebook-based Q4X Quantization using the Llama.cpp Framework on RISC-V Vector CPUs