How India Inc. can reduce energy demand with edge computing

Time of India

6 hours ago

Recently, AI-generated, Ghibli-style images took the internet by storm. But behind their charm lies an invisible environmental cost—water and energy consumption. Even Sam Altman, CEO of OpenAI, acknowledged the toll, tweeting: 'It's super fun seeing people love images in ChatGPT… But our GPUs are melting.'As artificial intelligence continues to evolve, the energy demands of its infrastructure are becoming a growing concern. Traditional AI relies heavily on massive, centralized data centers operating round-the-clock. These facilities, packed with thousands of servers running complex computations, also consume enormous energy for cooling to prevent overheating.
Currently, data centers account for roughly
2% of global electricity use
—a number poised to rise as AI models become more complex. For perspective, training a single advanced model like GPT-3 can use as much electricity as several
hundred homes
consume in a year.
So, the million-dollar question is: How can we continue to harness AI's potential while curbing its environmental impact? One of the most promising answers lies in
edge computing
.
Edge computing processes data closer to where it's generated—on devices such as smartphones,
IoT
sensors, and embedded systems—rather than routing everything through centralized cloud data centers. This shift cuts down on transmission energy and reduces dependence on cloud infrastructure, making AI deployments significantly more energy efficient.
This article explores why Indian enterprises must embrace edge AI to curb energy usage—and how advancements in chip design are driving more sustainable, local AI processing.
Centralized Data Centers: A Growing Energy Challenge
By 2026, data center electricity consumption is expected to exceed
1,000 terawatt-hours
—roughly equivalent to Japan's entire electricity demand. The explosion of data centers is straining global power grids. Beyond computation, these facilities require constant cooling—often powered by fossil fuels—contributing to rising carbon emissions and climate risk. Even with increased investment in renewables, the pace may not be enough to keep up with AI's surging energy needs.
How edge computing reduces energy use
Edge computing decentralizes workloads by processing data at the edge of the network or directly on devices. This reduces the burden on cloud infrastructure and lowers overall energy consumption.
Instead of continuously streaming data to remote servers, edge devices process data locally and send only essential insights. For example, an edge-enabled surveillance system can analyze footage in real time and transmit only alerts or key clips—saving substantial energy otherwise spent on transmission and storage.
Additionally, local processing reduces idle time caused by round trips to the cloud, further boosting energy efficiency.
Energy-efficient chipsets powering the edge
A new wave of energy-efficient AI chipsets and microcontrollers is enabling powerful edge applications—from wearables and autonomous systems to smart homes and industrial automation. These chips are purpose-built for high-efficiency AI processing, integrating features like neural accelerators and micro-NPUs in compact, low-power formats.
Optimized for tasks such as vision recognition, audio sensing, and real-time decision-making, these chipsets bring intelligence directly to the device. Techniques like adaptive power scaling, heterogeneous computing, and low-precision AI operations allow them to balance performance and energy efficiency—resulting in faster processing, lower memory usage, and longer battery life.
With built-in security features and compatibility across ecosystems, these chipsets are simplifying deployment of scalable, secure AI at the edge.
Techniques for building edge AI models
Edge AI models are designed to work within the limited power and resource constraints of edge devices, enabling real-time and accurate data processing. Key techniques include:
1. Model Compression and Simplification
Using quantization (reducing calculation precision) and pruning (removing unnecessary neural connections), developers can significantly shrink models. These lightweight versions consume less memory and power—without sacrificing accuracy.
2. Streamlined Architectures
Models like
MobileNet
for image recognition and
TinyBERT
for language tasks are built specifically for constrained devices, balancing low power consumption with performance.
3. Leveraging Pre-Trained Models
Platforms offering pre-trained models that can be fine-tuned for specific use cases enable businesses to integrate AI more efficiently. Embedding these models directly into chipsets allows for faster deployment of AI solutions with lower energy consumption—even without deep AI expertise. This minimizes the need for extensive customization and shortens go-to-market timelines.
For silicon vendors, offering chips with an ecosystem of ready-to-deploy models adds significant value. A chip preloaded with AI capabilities lets customers bypass development hurdles and start immediately.
Overcoming challenges in edge AI
Despite its advantages, edge AI must overcome a few key hurdles to scale effectively:
1. Hardware Constraints
Edge devices lack the compute, memory, and storage of cloud servers. Addressing this, demands continuous innovation in low-power, high-performance chip design.
2. Managing Complex Edge Ecosystems
The decentralized nature of edge computing means managing a vast network of devices. As IoT adoption grows, robust frameworks and tools are essential for coordination and scalability.
3. Ensuring Security
With sensitive data processed locally, security becomes non-negotiable. Techniques like secure boot, data encryption, and regular firmware updates are essential to maintaining trust and safeguarding information.
Conclusion
As AI becomes increasingly embedded in everyday life, sustainability must be at the forefront. Edge computing offers a powerful solution by moving intelligence closer to the data source—cutting energy use and easing pressure on central infrastructure.
For India Inc., edge AI is more than just a trend—it's a strategic imperative. To align with the Net Zero Scenario, emissions must fall by 50% by 2030. Edge AI is paving the way for smarter, greener, and more responsive solutions—and the time to act is now.