Achronix Semiconductor Corporation is a fabless semiconductor corporation based in Santa Clara, California, offering high-end FPGA-based data acceleration solutions, designed to address high-performance, compute-intensive and real-time processing applications. Achronix FPGA and eFPGA IP offerings are further enhanced by ready-to-use accelerator cards targeting AI, machine learning, networking and data center applications. All Achronix products are fully supported by a complete and optimized range of Achronix software tools called ACE, which enables customers to quickly develop their own custom applications.
Achronix has a global footprint, with sales and design teams across the U.S., Europe and Asia. In January 2021, Achronix entered into a definitive merger agreement with ACE Convergence Acquisition Corp. (Nasdaq: ACEV) in a transaction that would result in Achronix being listed on Nasdaq.
Latest Featured Content from Achronix
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FPGAs Advance Data Acceleration in the Digital Transformation Age
Acceleration is becoming a critical technology for today’s data-intensive world. Conventional processors cannot keep up with the demands of AI and other performance-intensive workloads, and engineering teams are looking to acceleration technologies for leverage against the deluge of data. In this episode of Chalk Talk, Amelia Dalton chats with Tom Spencer of Achronix about the current revolution in acceleration technology, and about specific solutions from Achronix that take advantage of leading-edge FPGAs, design IP, and even plug-and-play accelerator cards to address a wide range of challenges.
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Benefits of FPGAs & eFPGA IP in Futureproofing Compute Acceleration
In the quest to accelerate and optimize today’s computing challenges such as AI inference, our system designs have to be flexible above all else. At the confluence of speed and flexibility are today’s new FPGAs and e-FPGA IP. In this episode of Chalk Talk, Amelia Dalton chats with Mike Fitton from Achronix about how to design systems to be both fast and future-proof using FPGA and e-FPGA technology.
An FPGA-Based Solution for a Graph Neural Network Accelerator
Graph Neural Networks (GNN) drive high demand for compute and memory performance and a software only based implementation of a GNN does not meet performance targets. As a result, there is an urgent need for hardware-based GNN acceleration. While traditional convolutional neural network (CNN) hardware acceleration has many solutions, the hardware acceleration of GNN has not been fully discussed and researched. This white paper reviews the latest GNN algorithms, the current status of acceleration technology research, and discusses FPGA-based GNN acceleration technology.