DhruvaAI : Engineering Silicon Excellence

Scalable Inference Infrastucture for Edge Intelligence

Artificial intelligence is rapidly moving to the edge, where real-time decisions determine performance and safety. However, most edge hardware still faces memory bottlenecks, inefficient data movement, unstable power under sustained workloads, and rigid architectures where peak TOPS do not reflect real-world performance.

DhruvaAI eliminates these structural inference limitations by combining dataflow-optimized execution with a programmable abstraction layer, delivering sustained performance, deterministic latency, and a scalable compute architecture purpose-built for modern edge AI systems. Developers benefit not only from high efficiency, but also from simplified deployment using familiar AI frameworks and tools.

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The Industry UCIe Shift

DhruvaAI is being designed from the ground up as an inference-first silicon platform. Its dataflow-optimized execution model, SRAM-prioritized memory hierarchy, and hardware-level scheduling reduce external memory dependency and maintain stable performance under multi-stream workloads.

To simplify system design, DhruvaAI introduces a unified hardware abstraction layer that presents multiple chiplets as a single logical accelerator, eliminating the need for developers to manage interconnect complexity directly.

Native integration with UCIe enables modular chiplet expansion, while high-bandwidth sensor support ensures seamless system integration. The result is efficient, sustained inference performance — not just theoretical peak capability — delivered through a scalable yet developer-friendly architecture.

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Engineered for Scale

Edge AI workloads will continue to grow in complexity, scale, and concurrency.

DhruvaAI is architected as a modular inference platform that evolves with these demands. From single-chip deployments to scalable multi-chip systems, one architecture supports long product lifecycles without redesign.

Through a combination of hardware-native scalability and runtime-driven adaptability, DhruvaAI enables systems to grow in capability without increasing software complexity. Edge intelligence requires architectural advancement, not incremental acceleration. DhruvaAI defines that evolution while maintaining ease of adoption across product generations.

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DhruvaAI Accelerator SoC

A scalable edge inference processor integrating:

40–60 TOPS base AI compute.
Large on-chip SRAM banks.
LPDDR / DDR5 high-bandwidth memory interface.
PCIe Gen5 connectivity.
Native UCIe chiplet expansion fabric.
Integrated high-bandwidth sensor support.
Optimized pipelines for vision, transformer, and multimodal AI.
Virtual instruction layer for simplified, hardware-agnostic execution.

Designed as the compute backbone of intelligent edge systems, combining efficiency of dataflow with flexibility of programmable execution.

Inference-First Architecture

DhruvaAI is purpose-built for inference rather than adapted from CPU or GPU designs. Its dataflow-optimized execution, SRAM-prioritized memory hierarchy, and hardware scheduling reduce DRAM dependency while sustaining high utilization under multi-stream workloads.

To improve usability, DhruvaAI introduces a hierarchical execution model, where developers interact with high-level model representations while the underlying system maps workloads into optimized execution pipelines. While dataflow execution improves efficiency, the system relies on compiler and runtime support to translate models effectively.

This approach delivers predictable latency, stable performance, and efficient power behavior in real-world edge deployments—while reducing, though not entirely eliminating, the need for manual optimization.

Composable Scalability

AI workloads continue to grow in complexity and scale. With native UCIe integration, DhruvaAI enables expansion through AI compute chiplets, memory scaling, and domain-specific accelerators.

A unified runtime system manages workload distribution, memory allocation, and inter-chip communication, reducing the burden on developers. However, efficient scaling still depends on workload characteristics and software optimization.

This modular architecture allows performance to scale across deployments while maintaining architectural consistency across product tiers. It extends platform lifecycles and reduces the need for frequent redesign, though system-level tuning may still be required for optimal performance.

Software Ecosystem

DhruvaAI provides a comprehensive software stack designed to balance performance and usability:

Framework compatibility: PyTorch, TensorFlow, ONNX

Graph-based compiler: Converts models into optimized execution plans

Virtual instruction layer: Bridges models to hardware-efficient execution

Unified runtime engine: Handles scheduling, multi-stream workloads, and chiplets

Pre-built pipelines: Optimized for vision and transformer workloads

Profiling and debugging tools: Visibility into latency, memory, and performance

Fallback execution support: Ensures broad model compatibility

This integrated stack enables efficient execution and scalable deployment while simplifying development.

Our Vision - Founder’s Message

Artificial Intelligence is still at the beginning of its journey. While recent breakthroughs have transformed industries, the real potential of AI will be realized when intelligent systems can solve real-world problems autonomously and continuously improve through experience.

Today, most AI systems are trained in large data centers and deployed for inference in the field. This separation limits their ability to adapt to dynamic environments and real-time conditions.

The future of AI will be fundamentally different.

Intelligent systems will observe the world, learn from real-time data, correct themselves, and evolve continuously. AI will move beyond executing static models and become a living intelligence infrastructure embedded across the physical world.

Imagine a farmer installing an AI camera in a field. The system monitors crop health, predicts rainfall patterns, detects early disease signals, and recommends preventive actions. Over time it learns from seasons, environment, and outcomes—continuously improving its insights and helping farmers make better decisions.

Enabling this future requires a new generation of computing infrastructure capable of processing massive streams of sensor and device data efficiently and reliably.

At DhruvaAI Technologies, we are building next-generation AI computing platforms that begin with high-performance inference and evolve toward continuous, real-world learning systems. Our architecture is designed not just to run models efficiently, but to enable intelligent systems that observe, adapt, and improve over time. By combining dataflow-optimized execution, SRAM-centric memory architecture, and chiplet-based scalability through UCIe, DhruvaAI delivers sustained performance, efficient data movement, and modular scalability from edge deployments to large-scale systems.

DhruvaAI is building the silicon foundation for the next generation of intelligent systems.

— Founder
DhruvaAI Technologies Private Limited

Our Team

Prasad Mallampalli

Founder & CEO
— Driving next-generation dataflow AI.

Prasad Mallampalli is the Founder and CEO of DhruvaAI Technologies, building next-generation computing infrastructure for intelligent systems at the edge. With over 16 years of experience in semiconductor design and system architecture, he has led complex SoC development and large-scale integrations across global companies including Qualcomm, Intel, and Imagination Technologies, covering high-performance subsystems such as memory, PCIe, Ethernet, and multi-domain SoC architectures.

He operates at the intersection of architecture, execution, and system optimization, driving silicon from concept to signoff while leading cross-functional teams across design, verification, and backend, with expertise in low-power design, scalable integration, and production-ready semiconductor platforms.

He founded DhruvaAI with the conviction that current computing architectures are not designed for real-world AI, and is now developing inference-first computing platforms focused on efficient data movement, sustained performance, and scalable deployment to enable intelligent systems that operate reliably, adapt dynamically, and continuously evolve.

Sudershan Raghavachary Vuruputoor

Strategic Advisor
— Bridging AI innovation with scalable silicon execution.

Sudershan Raghavachary Vuruputoor is an accomplished semiconductor industry leader with 35+ years of experience in VLSI design, product engineering, and organizational leadership. He has held Director-level positions at globally recognized semiconductor companies, including SiMa.ai, ON Semiconductor, AMD, Nvidia, Analog Devices and Texas Instruments.

With deep expertise spanning AI edge computing, imaging solutions, DSP and large-scale SoC development, Sudershan brings strategic insight into building high-performance, power-efficient semiconductor products. His experience covers end-to-end product lifecycle—from architecture and design to execution and scaling global teams.

At DhruvaAI, Sudershan serves as a Strategic Advisor, guiding technology direction, product strategy, and industry engagement. His mentorship strengthens DhruvaAI’s vision of developing next-generation AI-driven silicon solutions for real-world applications.

Dr. Kailash Chandra Ray

Technical Advisor
— Pioneering research in dataflow AI architectures.

Dr. Kailash Chandra Ray is a Technical Advisor to DhruvaAI Technologies and an Associate Professor at IIT Patna, with extensive expertise in VLSI architecture, signal processing, and embedded systems. He brings a strong academic and research background with significant contributions in high-performance DSP architectures, RISC-V processors. His work spans both algorithmic innovation and hardware realization, including FPGA and ASIC implementations of complex architectures and fault-tolerant computing platforms.

Dr. Ray has authored numerous high-impact publications in leading journals and conferences, with research covering areas such as efficient VLSI architectures, edge computing systems, and machine learning-based signal analysis. His work bridges the gap between theoretical research and practical hardware implementation.

At DhruvaAI, Dr. Ray provides Technical guidance on architecture, algorithm-hardware co-design, and advanced system development. His academic insight and deep technical expertise strengthen DhruvaAI’s foundation in building scalable, high-performance AI computing platforms for real-world applications.

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Our engineering teams work as an extension of your organization, delivering silicon-proven solutions with predictable quality and accelerated time-to-market.

Location:

Arenesha, 6th Floor, The District,

Myscape Rd, Financial District,

Nanakramguda, Hyderabad, Telangana 500032

Email:

info@dhruvaai.com

Call:

+91 90324 96916