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Xilinx
4.2
based on 56 Reviews
Video summary
Company Overview
Company Locations
Working at Xilinx
Company Summary
Xilinx is the inventor of the FPGA, programmable SoCs, and now, the ACAP.They deliver the most dynamic processing technology in the industry.
Overall Rating
4.2/5
based on 56 reviews
11% above
industry average
Highly rated for
Company culture, Job security, Work-life balance
Work Policy
Monday to Friday
94% employees reported
Flexible timing
85% employees reported
No travel
90% employees reported
Day shift
100% employees reported
View detailed work policy
Top Employees Benefits
Cafeteria
5 employees reported
Job/Soft skill training
5 employees reported
Health insurance
5 employees reported
Office gym
4 employees reported
View all benefits
About Xilinx
Founded in2006 (19 yrs old)
India Employee Count51-200
Global Employee Count1k-5k
HeadquartersSan Jose, California, United States (USA)
Office Locations
Websitexilinx.com
Primary Industry
Other Industries
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Xilinx is the inventor of the FPGA, programmable SoCs, and now, the ACAP. Our highly-flexible programmable silicon, enabled by a suite of advanced software and tools, drives rapid innovation across a wide span of industries and technologies - from consumer to cars to the cloud. Xilinx delivers the most dynamic processing technology in the industry, enabling rapid innovation with its adaptable, intelligent computing.
Mission: Addressing the rapidly growing demands for programmability and intelligence with software, while enabling greater than 10X the bandwidth, 1/10th the latency and power, and flexible any-to-any connectivity with optimized hardware.
Vision: To shape the future and enable great technology that changes the way people live and work.
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Xilinx Ratings
based on 56 reviews
Overall Rating
4.2/5
How AmbitionBox ratings work?
5
28
4
19
3
7
2
2
1
0
Category Ratings
4.2
Company culture
4.2
Job security
4.2
Work-life balance
3.9
Skill development
3.8
Salary
3.8
Work satisfaction
3.5
Promotions
Xilinx is rated 4.2 out of 5 stars on AmbitionBox, based on 56 company reviews. This rating reflects a generally positive employee experience, indicating satisfaction with the company’s work culture, benefits, and career growth opportunities. AmbitionBox gathers authentic employee reviews and ratings, making it a trusted platform for job seekers and employees in India.
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Xilinx Reviews
Top mentions in Xilinx Reviews
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Compare Xilinx with Similar Companies
Change Company | Change Company | Change Company | ||
|---|---|---|---|---|
Overall Rating | 4.2/5 based on 56 reviews | 3.8/5 based on 1.1k reviews | 4.2/5 based on 1k reviews | 3.9/5 based on 634 reviews |
Highly Rated for | Work-life balance Job security Company culture | Salary | Work-life balance Company culture Skill development | No highly rated category |
Critically Rated for | No critically rated category | No critically rated category | No critically rated category | Promotions |
Primary Work Policy | - | Work from office 65% employees reported | Hybrid 82% employees reported | Work from office 62% employees reported |
Rating by Women Employees | 3.8 Good rated by 12 women | 3.9 Good rated by 170 women | 4.3 Good rated by 232 women | 3.8 Good rated by 58 women |
Rating by Men Employees | 4.4 Good rated by 38 men | 3.8 Good rated by 796 men | 4.2 Good rated by 695 men | 3.9 Good rated by 551 men |
Job security | 4.2 Good | 3.6 Good | 3.5 Good | 3.6 Good |
View more
Xilinx Salaries
Xilinx salaries have received with an average score of 3.8 out of 5 by 56 employees.
Senior Software Engineer
(25 salaries)
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₹21.1 L/yr - ₹42 L/yr
Design Engineer
(22 salaries)
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₹5 L/yr - ₹19.4 L/yr
Software Engineer
(20 salaries)
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₹4.5 L/yr - ₹17 L/yr
Product Application Engineer
(11 salaries)
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₹4.6 L/yr - ₹15.3 L/yr
Senior Software Engineer 2
(11 salaries)
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₹24 L/yr - ₹44 L/yr
Staff Engineer
(9 salaries)
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₹39.3 L/yr - ₹69 L/yr
Design Engineer II
(9 salaries)
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₹13 L/yr - ₹19.6 L/yr
PMO Analyst
(7 salaries)
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₹36 L/yr - ₹46 L/yr
Senior Product Application Engineer
(7 salaries)
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₹6.5 L/yr - ₹27.7 L/yr
Embedded Software Engineer
(7 salaries)
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₹6 L/yr - ₹6.5 L/yr
Xilinx News
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AI Accelerators in Embedded Systems
- AI tools have expanded into embedded systems like IoT sensors and portable medical devices, requiring efficient AI model execution with limited resources.
- New AI-specific hardware accelerators like NPUs, GPUs, DSPs, FPGAs, and microcontrollers are enhancing embedded systems technology.
- NPUs like ARM Ethos-U55 and U65 optimize machine learning applications in microcontrollers, supporting high-performance inference on battery-powered devices.
- GPUs, such as NVIDIA's Jetson Nano and Xavier NX, are useful for intensive workloads like facial recognition and real-time video analytics in embedded applications.
- DSPs offer real-time signal processing and now support AI models, making them a balanced choice for applications needing inference and signal processing.
- FPGAs are flexible for AI acceleration in embedded systems and can be customized for efficient execution of specific tasks with frameworks like Xilinx's Vitis AI.
- Microcontrollers with integrated AI accelerators like STM32 and ESP32-S3 offer cost-effective solutions for low-power applications such as IoT sensors or wearables.
- Choosing the right AI accelerator for an embedded project depends on factors like application type, performance needs, power consumption, and cost constraints.
- Open-source platforms like TensorFlow Lite, PyTorch Mobile, and Edge Impulse have facilitated the adoption of AI accelerators in embedded systems for IoT applications.
- RISC-V architecture is gaining traction for creating application-specific AI accelerators in embedded systems, offering flexibility in balancing performance, power consumption, and cost.
- AI accelerators in embedded systems cater to diverse needs, offering unique features for specific applications and evolving to enable AI integration in various industries.
Embedded | 5 Jun, 2025
PointODE: Lightweight Point Cloud Learning with Neural Ordinary Differential Equations on Edge
- PointODE is introduced as a parameter-efficient ResNet-like architecture for point cloud feature extraction on embedded edge devices.
- Neural Ordinary Differential Equation (Neural ODE) is used to compress PointODE by reusing the same parameters across MLP blocks.
- PointODE-Elite, a lightweight version with 0.58M trainable parameters, is designed with a dedicated accelerator for embedded FPGAs.
- The accelerator implemented on a Xilinx ZCU104 board speeds up feature extraction by 4.9x compared to the ARM Cortex-A53 CPU, leading to faster inference and better energy-efficiency.
Arxiv | 3 Jun, 2025
AMD’s Bold Move into the Embedded Edge: Leading the AI Frontier
- The rise of edge computing allows devices to process information locally in real time, leading to reduced latency, enhanced privacy, security, and efficiency.
- AMD is focusing on the embedded edge market by adopting a modular approach to offer customized solutions for various industries, gaining advantage over monolithic chip solutions.
- With the acquisition of Xilinx, AMD aims to disrupt Intel's dominance in the embedded market by offering adaptive computing platforms optimized for AI workloads, leading in speed and energy efficiency.
- AMD's strategic focus on edge AI and adaptive computing positions it to lead the innovation in embedded edge computing, indicating its strong potential for long-term growth and influence in shaping the future of intelligent decision-making.
Medium | 3 May, 2025
NSFlow: An End-to-End FPGA Framework with Scalable Dataflow Architecture for Neuro-Symbolic AI
- Neuro-Symbolic AI (NSAI) is an emerging paradigm that integrates neural networks with symbolic reasoning to enhance the transparency, reasoning capabilities, and data efficiency of AI systems.
- The execution of NSAI tasks on existing hardware remains challenging due to their heterogeneous computing kernels, high memory intensity, and unique memory access patterns, which necessitates a versatile acceleration framework tailored to NSAI workloads.
- NSFlow is an FPGA-based acceleration framework designed to achieve high efficiency, scalability, and versatility across NSAI systems.
- In evaluations, NSFlow outperforms existing hardware such as Jetson TX2, GPU, TPU-like systolic array, and Xilinx DPU, achieving significant speedups and scalability.
Arxiv | 29 Apr, 2025
On-Device Qwen2.5: Efficient LLM Inference with Model Compression and Hardware Acceleration
- An efficient framework, Qwen2.5, is presented for deploying LLMs on edge devices.
- Qwen2.5-0.5B model is deployed on the Xilinx Kria KV260 edge platform.
- The proposed approach includes Activation-aware Weight Quantization (AWQ) and FPGA-accelerated execution pipelines.
- The framework achieves a model compression rate of 55.08% and a throughput of 5.1 tokens per second.
Arxiv | 25 Apr, 2025
Building a Custom Zynq-7000 SoC Development Board from the Ground Up
- In this series of 23 YouTube videos, [Rich] demonstrates building a custom AMD Zynq-7000 SoC development board from scratch.
- The board includes various peripherals such as HDMI, USB, and DDR RAM, and is capable of booting PetaLinux.
- The Zynq-7000 SoC consists of an ARM Cortex-A9 Based APU and an FPGA, and is part of the Xilinx product line.
- The developer also has a follow-up playlist where he explores further enhancements to the Zynq-7000 board.
Hackaday | 20 Apr, 2025
Towards On-Device Learning and Reconfigurable Hardware Implementation for Encoded Single-Photon Signal Processing
- Deep neural networks (DNNs) enhance the accuracy and efficiency of reconstructing key parameters from time-resolved photon arrival signals recorded by single-photon detectors.
- To address the challenges of frequent network retraining and data transfer, an online training algorithm called One-Sided Jacobi rotation-based Online Sequential Extreme Learning Machine (OSOS-ELM) is proposed.
- OSOS-ELM demonstrates comparable accuracy to traditional ELM and proves to be more hardware-efficient.
- A holistic computing prototype is implemented using a Xilinx ZCU104 FPGA and a NVIDIA Jetson Xavier NX GPU to validate the performance of OSOS-ELM in single-photon signal analysis.
Arxiv | 15 Apr, 2025
Microchip Pioneer Honored with Computing Breakthrough Award
- Jingsheng Jason Cong, a leading engineering expert, receives the 2024 ACM Charles P. 'Chuck' Thacker Breakthrough in Computing Award for his notable contributions to computing technologies.
- Cong's work on field-programmable gate arrays (FPGAs) has significantly impacted customizable computing, making him a key figure in the engineering domain.
- His efforts have simplified FPGA programming through the development of design automation tools, democratizing access to this technology for a wider audience.
- Cong's innovative algorithms have revolutionized FPGA configuration, enabling programmers to use high-level languages like C and C++ instead of hardware description languages.
- In the late 1990s, Cong addressed the mapping logic challenge on FPGAs, leading to the creation of Aplus Design Automation and commercial FPGA synthesis tools.
- His founding of AutoESL, focusing on high-level synthesis tools for FPGAs, has further advanced customizable computing and integrated into AMD/Xilinx's FPGA ecosystem.
- Cong's research extends to specialized computing solutions like artificial intelligence algorithms, showcasing superior performance compared to CPU-based systems.
- His mentorship and dedication to nurturing future engineers highlight his commitment to the field, echoing the spirit of the award's namesake, Chuck Thacker.
- The ACM Breakthrough in Computing Award recognizes both technical excellence and mentorship impact, emphasizing the importance of practical implementation of theoretical knowledge.
- The award will be presented at the ACM's Annual Awards Banquet, recognizing Cong's contributions to computing technology evolution and future advancements.
- Cong's work sets a critical foundation for the evolution of integrated circuits, enhancing versatility, efficiency, and computational power across diverse applications.
Bioengineer | 10 Apr, 2025
Balancing Robustness and Efficiency in Embedded DNNs Through Activation Function Selection
- Machine learning-based embedded systems need to be robust to perturbations caused by soft errors.
- The choice of activation functions (AFs) impacts accuracy, trainability, compressibility, and error resilience.
- Bounded AFs are explored to enhance robustness against parameter perturbations.
- Experiments are conducted on an AMD-Xilinx's KV260 SoM.
Arxiv | 8 Apr, 2025
Efficient FPGA-accelerated Convolutional Neural Networks for Cloud Detection on CubeSats
- Researchers have implemented FPGA-accelerated convolutional neural network (CNN) models for cloud detection on CubeSats.
- The models leverage Xilinx's Vitis AI (VAI) framework and Deep Learning Processing Unit (DPU) on a Zynq UltraScale+ MPSoC.
- Model parameters and floating-point operations were significantly reduced through channel pruning.
- The models achieved high accuracy post-FPGA integration and demonstrated strong real-time inference capabilities with low power consumption.
Arxiv | 8 Apr, 2025
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Xilinx FAQs
When was Xilinx founded?
Xilinx was founded in 2006. The company has been operating for 19 years primarily in the Semiconductors sector.
Where is the Xilinx headquarters located?
Xilinx is headquartered in San Jose, California and has an office in Hyderabad / Secunderabad.
How many employees does Xilinx have in India?
Xilinx currently has approximately 70+ employees in India.
Does Xilinx have good work-life balance?
Xilinx has a work-life balance rating of 4.2 out of 5 based on 50+ employee reviews on AmbitionBox. 84% employees rated Xilinx 4 or above on work-life balance. This rating reflects the company's efforts to help employees maintain a healthy balance between their personal and professional lives. We encourage you to read Xilinx work-life balance reviews for more details.
Is Xilinx good for career growth?
Career growth at Xilinx is rated fairly well, with a promotions and appraisal rating of 3.5. 84% employees rated Xilinx 4 or above, while 16% employees rated it 3 or below on promotions / appraisal. Though the sentiment is mixed for career growth, majority employees have rated it positively. We recommend reading Xilinx reviews for more detailed insights.
What are the pros and cons of working in Xilinx?
Working at Xilinx comes with several advantages and disadvantages. It is highly rated for company culture, job security and work life balance. However, it is poorly rated for promotions / appraisal, based on 50+ employee reviews on AmbitionBox.
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