By optimizing the architecture of specific domains
Realize maximum throughput and ultra-low latency
By reconfiguring hardware
Faster adaptation to constantly evolving workloads
Cross computable
Various acceleration use cases for storage and network acceleration
Accelerate video and image workloads, such as video transcoding, real-time streaming, and image processing
Using the Xilinx ecosystem to accelerate a wide range of database and data analysis workloads
Accelerate a range of workloads, such as accelerating ticket to trade, pre trade risk checks, and market data processing
Accelerate a wide range of basic applications, from genome sequencing to weather modeling, and then to applications that enhance national security with ultra-high processing speed and flexibility
Accelerate demanding network workloads such as encryption, compression, filtering, deep packet detection, and virtual switching and routing
Significantly improves the efficiency of system level TCO and storage workloads, such as erasure encoding, compression, and hash/deduplication
Whole application acceleration - using domain specific architecture to accelerate AI inference, preprocessing/post-processing, and other critical workloads
For low latency artificial intelligence inference, Xilinx can achieve maximum throughput across a wide range of networks and data types under low latency conditions
Data centers are increasingly adopting artificial intelligence to manage various tasks from device monitoring to server optimization
FPGA based adaptive computing is at the core of the data center and in many cases, is the most efficient way to run complex AI workloads
The most cost-effective solution. Read e-books to learn how adaptive computing can help accelerate
Exploring the ecosystem of partner applications
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