
EnCharge AI provides efficient, low‑power AI computing solutions that let businesses run advanced models from edge devices to the cloud. The company designs analog in‑memory computing chips and pairs…

EnCharge AI provides efficient, low‑power AI computing solutions that let businesses run advanced models from edge devices to the cloud. The company designs analog in‑memory computing chips and pairs…
Founded: 2022 (Princeton University spinout)
Headquarters: Santa Clara (evidence: company described as Santa Clara–based)
Product: Analog in-memory AI accelerators with hardware/software stack for edge-to-cloud
Recent funding: Series B (Feb 13, 2025) > $100M led by Tiger Global
Total funding reported: $162.9M (currency: USD)
Energy-efficient AI inference for edge and cloud (power-constrained applications, on-device/edge deployments).
2022
Data and Analytics
21700000
Reported $21.7M; participants included AlleyCorp, Scout Ventures, Silicon Catalyst Angels and others.
22600000
Reported $22.6M round (brought total to ~$45M at that time); participants included VentureTech Alliance, RTX Ventures, ACVC Partners, Anzu Partners, S5V, AlleyCorp, Scout and Silicon Catalyst Angels.
100000000
Reported as more than $100M Series B led by Tiger Global.
“Includes strategic and financial investors such as Tiger Global, VentureTech Alliance (TSMC-related), RTX Ventures, Samsung Ventures and a mix of VC and corporate backers”
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SoC Architect – Chiplet-Based Systems
Locations: Bangalore /Remote ( any where India )
Job Description:
SoC Architect – Chiplet-Based Systems
Job Description: Join us as a SoC Architect focusing on chiplet-based AI systems.
You will help define and drive the architecture of modular compute platforms using chiplet integration.
The position centers around architecting high-bandwidth I/O and memory connectivity between chiplets, ensuring efficient partitioning of functionality, and defining scalable inter-chiplet protocols for future-generation AI accelerators.
This role involves working closely with packaging, PHY, and interconnect experts to define die-to-die interfaces (e.g., UCIe, BoW, or custom links) and orchestrating integration across logic, memory, and I/O chiplets.
You’ll also own subsystem architecture for PCIe, RISC-V clusters, and memory hierarchies, while ensuring coherence and latency/power-optimized communication between disaggregated components.
Responsibilities: • Define the SoC architecture for chiplet-based AI inference platforms, including inter-chiplet data paths, protocols, and synchronization strategies. • Drive partitioning decisions between compute, I/O, memory, and control chiplets.
• Architect PCIe and DMA interfaces that interact with host systems and bridge to chiplet domains. • Specify die-to-die interconnect requirements (e.g., bandwidth, latency, power) and collaborate with packaging and PHY teams.
• Integrate and verify third-party IPs for I/O, memory, and inter-chip communication.
• Support bring-up and debug of multi-chip systems.
Required Background: • BS/MS/Ph.D. in EE or CS with 10-25+ years of SoC or multi-die system experience.
• Hands-on experience in chiplet-based design, including familiarity with UCIe, EMIB, Foveros, or similar packaging technologies.
• Strong understanding of modular SoC partitioning and die-to-die interconnect architectures.
• Experience in PCIe Gen 4/5, RISC-V subsystems, and high-performance memory interfaces (LPDDR4/5, HBM).
• Familiarity with chiplet-aware system bring-up and verification methodologies.
• SystemVerilog/UVM experience and knowledge of system-level test/debug strategies.
Contact:
Uday
Mulya Technologies
muday_bhaskar@yahoo.com
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