AI / Machine Learning
Accelerating AI, machine learning, and deep learning developments with flexible network-on-chip (NoC) architectures, timing closure, and bandwidth optimization
Overview
Overview
AI applications are becoming critical in our hyperconnected world, driven by the growing demand for generative AI. According to Futurum Intelligence, the AI chipset market for processors and accelerators in the data center segment was valued at $38 billion in 2023 and is projected to grow at a 30% CAGR, reaching $138 billion by 2028. This surge is fueled by the rapid adoption of generative AI models and chipmakers’ focus on optimizing AI inferencing chip support.
GPUs dominate the data center AI market, holding 92% of the market share and accounting for 74% of AI chipsets used in data centers. The GPU market, led by companies like Nvidia, is expected to grow at a 30% CAGR, reaching $102 billion by 2028. CPUs also play a crucial role in AI processing, with a 28% CAGR forecast, growing from $7.7 billion in 2023 to $26 billion by 2028. Hyperscale cloud providers, such as Google and AWS, are significant drivers of AI chipset purchases, accounting for 43% of the market in 2023, with a forecast to grow to 50% by 2028. Custom cloud accelerators, including Google TPU and AWS Trainium, are increasingly essential in scaling AI infrastructure.
As the demand for AI chipsets grows, key development challenges arise, including balancing power consumption and performance, managing limited memory and processing resources, addressing data security and privacy concerns, and optimizing connectivity and communication protocols. Additionally, deploying AI algorithms on low-power devices requires careful algorithmic optimization to ensure efficient performance.
Different architectures are being explored to achieve the best performance per watt. While CPUs and GPUs are expected at the low end, more advanced custom designs, such as Google TPU, are deployed for high-end applications. However, these architectures push chip size and complexity, creating potential issues with timing closure, traffic congestion, and power management. As a result, on-chip networks for machine learning are becoming increasingly important, helping architects maximize performance and efficiency while managing the spatial distribution of machine learning workloads.
Arteris, which has long supported edge AI inferencing, offers solutions like the XL Option for FlexGen and FlexNoC, which allows developers to create flexible network architectures. These solutions address critical timing closure issues, bandwidth demands, and memory controller utilization challenges, which are key to achieving high performance in increasingly complex systems.
Advantages
Advantages
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Scalability
Create highly scalable Ring, Mesh, and Torus topologies with highly efficient unlike black box compiler approaches, SoC architects can edit generated topologies and also optimize each individual network router, if desired.
Bandwidth
Increase on-chip and off-chip bandwidth with HBM2 and multichannel memory support, multicast/broadcast writes, VC-Link™ Virtual Channels, and source-synchronous communications.
Low Power
Fewer wires and fewer gates consume less power, breaking communication paths into smaller segments allows to power only active segments, and simple internal protocol allows aggressive clock gating.
Introducing Network-on-Chip tiling to accelerate semiconductor designs for AI applications
NoC tiling is an emerging trend in SoC design that uses proven, robust network-on-chip IP to facilitate scaling, condense design time, speed testing and reduce design risk.
It empowers SoC architects to quickly create modular, scalable AI designs, enabling faster integration, verification and optimization.
NoC tiling advantages – available with FlexGen, FlexNoC and Ncore
- Scalable Performance
- Power Reduction
- Dynamic Reuse
Lorem Ipsum
Arteris NoC Tiling Innovation
Addresses AI Use Cases Across Markets
Artificial Intelligence / Machine Learning
Partners
Andes Technology is a founding and premier member of RISC-V International and a leading supplier of high-performance/low-power RISC-V processor IP. Andes Technology and Arteris partner to advance innovation for RISC-V based SoC designs for AI, 5G, networking, mobile, storage, AIoT and space applications. The Andes QiLai RISC-V platform is a development board with a QiLai SoC featuring the Andes’ RISC-V processor IPs along with Arteris FlexNoC interconnect IP used for on-chip connectivity.
In March 2024, Arteris delivered on its previously announced collaboration with Arm to speed up automotive electronics innovation with an emulation-based validation system for Armv9 and CHI-E-based designs to speed up innovation in automotive electronics for autonomous driving, advanced driver-assistance systems (ADAS), cockpit and infotainment, vision, radar and lidar, body and chassis control, zonal controllers and other automotive applications. Arteris aligned its roadmap with Arm to enable designers to get to market faster with an optimized and pre-validated high-bandwidth, low-latency Ncore cache coherent interconnect IP for Arm’s Automotive Enhanced (AE) compute portfolio. The partnership helps customers realize SoCs with high performance and power efficiency for safety-critical tasks while reducing project schedules and costs. It offers mutual customers a greater choice of safe, integrated, and optimized automotive solutions to enable faster time to market via seamless integration and optimized flows with the highest quality of results, enabling ISO 26262 systems with the highest automotive safety integrity levels (ASIL).
The Damo Wujian Alliance, spearheaded by Damo Academy (an affiliate of Alibaba Group), is an ecosystem alliance driving the adoption and development of the RISC-V instruction-set architecture. The coalition focuses on high-performance System-on-Chip (SoC) designs, particularly in edge AI computing. As part of the alliance, Arteris plays a pivotal role by enabling the integration of Damo Academy’s / T-Head’s Xuantie RISC-V processor IP cores with its Ncore cache coherent network-on-chip (NoC) system IP, resulting in efficient data transport architectures within cores and between chips, enabling cutting-edge applications in AI, machine learning, and more.
Fraunhofer IESE is one of 76 institutes and research units of the Fraunhofer-Gesellschaft. Together they have a major impact on shaping applied research in Europe and contribute to Germany’s competitiveness in international markets. Our partnership with Fraunhofer enables early architecture analysis of DRAM performance effects on network-on-chip performance through connections between Ncore and FlexNoC SystemC simulation and the DRAMSys DRAM modeling and simulation framework.
Arteris and SiFive have partnered to accelerate the development of edge AI SoCs for consumer electronics and industrial applications. The partnership combines SiFive’s multi-core RISC-V processor IP and Arteris’ Ncore cache coherent interconnect IP, providing high performance and power efficiency with reduced project schedules and integration costs. The collaboration has led to the development of the SiFive 22G1 X280 Customer Reference Platform, incorporating SiFive X280 processor IP and Arteris Ncore cache coherent interconnect IP on the AMD Virtex UltraScale+ FPGA VCU118 Evaluation Kit.
Semidynamics is a provider of fully customizable RISC-V processor IP and specializes in high bandwidth, high-performance cores with Vector Units, Tensor Units and Gazzillion, and targeted at machine learning and AI applications. Our collaboration enhances the flexibility and highly configurable interoperability of processor IP with system IP, aiming to deliver Integrated and optimized solutions with focus on accelerating artificial intelligence, machine learning and high-performance computing (HPC) applications.
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