Daikoku Innovations (Daikoku) is a strong product design services company headquarterein Bengaluru and catering to the global market, including India. The founders saw a huge gap in the market for cutting edge product design services which is commercially viable, both for the customers and Daikoku. There were immense challenges when companies were looking for design partners to complement their existing design teams or completely outsource cutting edge product designs. Intellectual property rights (IPR) also were of immense concern. Daikoku allays all these concerns via their trustworthy process and approach towards engaging a potential customer, understanding the design challenge, ideating, creating a design plan along with feasibility study, design & development, test & verification, validation& collateral production, manufacturing and seamless transfer of IPR.
Machine Learning Applications Based on FPGA
Introduction
Machine Learning (ML) is transforming industries by enabling systems to analyze data, recognize patterns, and make intelligent decisions. While CPUs and GPUs have been the traditional engines behind ML workloads, Field-Programmable Gate Arrays (FPGAs) are emerging as a promising alternative. With their flexibility, parallelism, and power efficiency, FPGAs are ideally suited for ML applications that demand low latency, high throughput, and edge deployment.
Key Machine Learning Applications on FPGA
1. Image and Video Processing – Real-time object detection and face recognition.
2. Natural Language Processing (NLP) – FPGA accelerators for transformers and quantized NLP models.
3. Healthcare and Biomedical Devices – On-device analysis for ECG/MRI without cloud dependence.
4. Industrial IoT and Predictive Maintenance – Edge ML for anomaly detection in factories. 5. Financial Applications – Fraud detection and high-frequency trading with ultra-low latency. 6. Autonomous Systems and Robotics – Power-efficient ML for drones, vehicles, and robots.
Innovative and Emerging Applications
1. Federated Learning on Edge FPGAs
Instead of sending sensitive data to the cloud, FPGAs at the edge can train local ML models and contribute updates to a central server. This enables privacy-preserving AI in healthcare, defense, and finance.
2. Brain-Computer Interfaces (BCIs)
FPGAs are being explored for neural signal decoding in real time. With their low latency, they can power next-generation assistive devices, such as prosthetics controlled by thought or communication aids for patients with paralysis.
3. 5G and 6G AI-Driven Networks
Modern telecom systems need AI for spectrum allocation, traffic prediction, and beamforming. FPGAs in base stations can run ML models that dynamically optimize network performance in microseconds, something GPUs cannot achieve.
4. Smart Agriculture
Edge FPGAs equipped with ML can analyze drone or sensor data for crop health, irrigation control, and pest detection in real time—without requiring constant cloud connectivity, which is crucial in remote areas.
5. Space and Aerospace AI
In satellites or deep-space probes, GPUs consume too much power. FPGAs with ML accelerators enable autonomous navigation, anomaly detection, and planetary surface mapping while meeting strict radiation and power constraints.
6. Generative AI at the Edge
Lightweight generative AI models (for synthetic speech, images, or anomaly simulation) can be deployed on FPGAs for real-time operation in AR/VR headsets, wearable devices, or creative tools without depending on massive GPUs.
7. Cybersecurity and Intrusion Detection
FPGAs can implement AI-driven network intrusion detection systems (NIDS) that analyze massive amounts of real-time traffic to detect anomalies or cyber-attacks with sub-millisecond response times.
8. Smart Energy Systems
In renewable energy grids, FPGAs with ML can optimize power distribution, predict equipment faults, and balance loads dynamically. This is vital for smart grids, wind farms, and solar power plants where latency and reliability are critical.
“We work on FPGA based solutions for pest control with Drone which adopts ML” says Sudarshan NS, founder and President of Daikoku Innovations
Future outlook
With the rise of edge AI, federated learning, and 6G networks, FPGAs will play a central role in real time, secure, and energy-efficient ML. Industry leaders (AMD/Xilinx, Intel, Lattice) are driving FPGA AI ecosystems with pre-optimized IPs and frameworks. Integration of FPGAs into heterogeneous computing platforms alongside CPUs and GPUs will further accelerate innovation.
Daikoku Innovations, working on Drone and Smart Energy systems with ML. Drone’s ML application has innovative 2mS decision making in pest control.
Pest control process includes:
Data Collection: Drones capture high-resolution multispectral or hyperspectral images of crops. Sensors detect plant stress patterns, leaf discoloration, or insect infestations.
Onboard ML Processing (FPGA-Accelerated): Instead of sending raw data to the cloud, the FPGA onboard the drone runs ML inference models.
FPGAs provide real-time detection of pests or crop diseases with low latency (less than 2mS). Targeted Pest Control
• When pests are detected, the drone triggers precision spraying of pesticides only where needed. • This reduces pesticide use by up to 70%, lowering costs and minimizing environmental damage.
