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Semiconductor R&D for edge AI
Mucore Technology is developing accelerator IP, evaluation modules, and runtime software for industrial cameras, autonomous drones, always-on sensors, and embedded systems that need efficient local inference.
Our long-term ambition: grow into an Indian SoC R&D company capable of designing advanced chips, built on practical engineering milestones.
Accelerator core, memory plan, firmware, and model runtime validated together before silicon.
Product stack
The first Mucore platform is an edge AI accelerator program for product teams that need local inference inside strict power, latency, and cost envelopes. The goal is not a generic benchmark chip; it is hardware that can be evaluated against real sensor workloads.
Configurable compute blocks for convolution, matrix, sparse, and streaming sensor workloads.
A partner-facing board for testing camera, audio, and sensor pipelines before custom silicon.
Model conversion, profiling, scheduling hooks, and APIs for embedded deployment teams.
Architecture
Keep activations and weights close to compute so edge systems spend less energy moving data.
Support camera, audio, vibration, and biosignal pipelines where data arrives continuously.
Favor predictable response for robotics, industrial safety, and on-device decision loops.
Expose profiling, scheduling, and model constraints clearly so developers can ship real products.
Our direction
We are starting with focused edge AI accelerator IP, evaluation modules, and software for real sensor workloads. The long-term goal is much bigger: build the Indian engineering capability to design increasingly capable SoCs, with the ambition to work toward advanced process nodes as our expertise, partnerships, and validation mature. We intend to earn that future through measurable steps in architecture, verification, emulation, software, and silicon.
Explore accelerator IP for vision and multi-sensor inference under real power and latency constraints.
Move from workload models toward simulation, FPGA evaluation, and a considered path to silicon.
Plan the runtime and developer tooling alongside the architecture, so products can use local intelligence.
Innovation ecosystem
We engage with government and industry innovation challenges to explore where indigenous edge AI can solve meaningful problems. We take part in initiatives such as T-Hub programs and the DRDO Dare to Dream 5.0 Innovation Contest.
Our focus is on learning from real requirements and building toward demonstrable, deployable technology.
Where it fits
Inspection, anomaly detection, safety zones, and machine monitoring without continuous cloud video.
Low-power perception for crop scouting, infrastructure inspection, and navigation support.
Keyword, vibration, gesture, and biosignal inference for battery-constrained devices.
Driver monitoring, cabin sensing, ADAS preprocessing, and local safety intelligence.
Development program
Choose the first deployment target, build the accelerator model, and validate real sensor data.
Build and demonstrate a partner-facing board with firmware, runtime tools, and benchmarks on representative sensor workloads.
Freeze the core IP, complete verification planning, and prepare for a low-risk first ASIC program.
Move from an accelerator block to integrated processors, domain SoCs, and high-end AI platforms.
Company journal
Contact
We want to hear from OEMs, industrial teams, robotics builders, drone companies, investors, and engineers with real edge inference constraints.