markets
We accelerate intelligent compute and address hyper-efficient data movement needs across diverse markets.
Baya Systems’ unified fabric technology is highly versatile, designed to meet the demands of data-intensive applications across diverse markets. Scalability, adaptability and chiplet readiness are key to meet the critical demands of these applications.​
Data Center Market needs
- Require massive, scalable compute combining CPUs with high-bandwidth, petabyte-scale AI acceleration.
- Trend toward processing disaggregation is driving a greater diversity of chips and chiplets.
- Require large-scale coherent CPU computations for hyperscale and enterprise computing applications.
- Baya’s WeaveIP advanced system IP deliver high data throughput at ultra-low latency, featuring Reliability, Availability and Serviceability (RAS), Quality of Service (QoS) and chiplet-readiness to meet the stringent demands of advanced data center applications.
Infrastructure market needs
- Involves diverse applications such as Content Delivery Networks (CDNs), Small Cells (SLAMs) and NanoCells.
- Requires the cooperative and shared processing between CPUs and accelerators (GPUs, FPGAs, ASICs).
- Packet processing, baseband processing.
- 5G radio access network solutions prioritize efficient data movement with tight thermal budgets.
- With demanding latency and bandwidth requirements.
- Co-existence of multiple microservices and applications necessitates an efficient underlying network fabric.
- Baya’s WeaveIP advanced system IP empower applications in wireless and wired networking infrastructure.
- Designed for scalability, adaptability and chiplet-readiness, WeaveIP is ideal for many-core and thread-rich systems, delivering unmatched performance and flexibility.
AI Acceleration market needs
- AI acceleration in servers have extreme data bandwidth requirements up to several terabytes per second.
- Demands orders of magnitude improvements in performance using less power to process large volumes of data faster.
- Requires scalability to support continually-evolving AI models; see demand for scalable chiplet architectures.
- Requires low-latency connections since AI applications are highly sensitive to latency.
- AI workloads can be vulnerable to the networking effects, especially multicast traffic, which limit performance scalability.
- Baya’s WeaveIP advanced system IP supports AI accelerators designed for high-throughput, parallelized data processing.
- Baya’s unified fabric is chiplet-ready to support their increased use for scaling AI acceleration.
- Baya’s FabricStudio allows designers to optimize design to meet KPIs that support AI workload needs.
- Network level multicast, interface virtualization, clustering and near unlimited scaling are supported.
Automotive market needs
- Advanced Driving Assistance Systems (ADAS), autonomous driving and cockpit solutions are driving high-performance compute needs with a trend toward integration.
- System-on-chips (SoCs) require seamless integration of various coherent and non-coherent systems.
- Chiplets are emerging in automotive to support integration in the central vehicle computer.
- Power efficiency is critical, especially in electric vehicles (EV) where range is always a concern.
- Baya’s unified fabric is ready to support multiple protocols and coherency needs simultaneously.
- Baya’s unified fabric scales to support on-chip, chiplet and multi-chip solutions seamlessly.
- Physical-aware design allows for optimization for data movement power efficiency.
IoT market needs
- Diverse market needs to empower Edge Servers, Home Gateways, Networking Hubs and complex Consumer Devices.
- Typically require compact, heterogeneous compute platforms.
- Low power is needed in battery-operated and constrained environments.
- Robotics and industrial controls require heterogeneous processing with data coherency.
- Baya’s FabricStudio can create power-optimized fabrics to target low-power applications.
- Multi-core, heterogeneous processing with diverse interfaces and protocol supported with unified fabric.
