New Jayhawk platform capitalizes on innovative energy efficient chiplet interconnects to improve performance and reduce data center energy consumption
SANTA CLARA, Calif.–(BUSINESS WIRE)–Today, d-Matrix, a leader in high-efficiency AI-compute and inference processors, announced Jayhawk, the industry’s first Open Domain-Specific Architecture (ODSA) Bunch of Wires (BoW) based chiplet platform for energy efficient die-die connectivity over organic substrates. Building on the back of the Nighthawk chiplet platform launched in 2021, the 2nd generation Jayhawk silicon platform further builds the scale-out chiplet based inference compute platform. d-Matrix customers will be able to use the inference compute platforms to manage Generative AI applications and Large Language Model transformer applications with a 10-20X improvement in performance.
Large transformer models are creating new demands for AI inference at the same time that memory and energy requirements are hitting physical limits. d-Matrix provides one of the first Digital In-Memory Compute (DIMC) based inference compute platforms to come to market, transforming the economics of complex transformers and Generative AI with a scalable platform built to handle the immense data and power requirements of inference AI. Improving performance can make energy-hungry data centers more efficient while reducing latency for end users in AI applications.
“With the announcement of our 2nd generation chiplet platform, Jayhawk, and a track record of execution, we are establishing our leadership in the chiplet ecosystem,” said Sid Sheth, CEO of d-Matrix. “The d-Matrix team has made great progress towards building the world’s first in-memory computing platform with a chiplet-based architecture targeted for power hungry and latency sensitive demands of generative AI.”
d-Matrix’s novel compute platform uses an ingenious combination of an in-memory compute-based IC architecture, sophisticated tools that integrate with leading ANN models, and chiplets in a block grid formation to support scalability and efficiency for demanding ML workloads. By using a modular chiplet-based approach, data center customers can refresh compute platforms on a much faster cadence using a pre-validated chiplet architecture. To enable this, d-Matrix plans to build chiplets based on both BoW and UCIe based interconnects to enable a truly heterogeneous computing platform that can accommodate 3rd party chiplets.
“d-Matrix has moved quickly to seize the chiplet opportunity, which should give them a first-mover advantage,” said Karl Freund, Founder and Principal Analyst at Cambrian-AI Research. “Anyone looking to add an AI accelerator to their SoC design would do well to investigate this new approach for efficient AI.”
The Jayhawk chiplet platform features:
- 3mm, 15mm, 25 mm trace lengths on organic substrate
- 16 Gbps/wire high bandwidth throughput
- 6-nm TSMC process technology
- <0.5 pJ/bit energy efficiency
Jayhawk is currently available for demos and evaluation. d-Matrix will be showcasing the Jayhawk platform at the Chiplet Summit Jan 24-26 in San Jose, CA.
d-Matrix is building a new way of doing datacenter AI inferencing at scale using in-memory computing (IMC) techniques with chiplet level scale-out interconnects. Founded in 2019, d-Matrix has attacked the physics of memory-compute integration using innovative circuit techniques, ML tools, software and algorithms; solving the memory-compute integration problem, which is the final frontier in AI compute efficiency. Learn more at dmatrix.ai.