
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 them with hardware and software systems to accelerate dense matrix operations and reduce compute, power, and space requirements. Its product stack targets edge-to-cloud deployments and power‑constrained applications, integrating with existing AI workflows and system software for inference and model deployment. Founded in 2022 by semiconductor and AI systems veterans, EnCharge reports large-scale production experience, patents, and sustainability-focused efficiency gains.

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 them with hardware and software systems to accelerate dense matrix operations and reduce compute, power, and space requirements. Its product stack targets edge-to-cloud deployments and power‑constrained applications, integrating with existing AI workflows and system software for inference and model deployment. Founded in 2022 by semiconductor and AI systems veterans, EnCharge reports large-scale production experience, patents, and sustainability-focused efficiency gains.
Founded: 2022
Headquarters: Santa Clara, California
Tech focus: Analog in-memory AI accelerators and software for edge-to-cloud
Recent funding: Series B > $100M (announced Feb 2025)
Energy- and space-constrained AI inference acceleration for edge-to-cloud deployments.
2022
Data and Analytics
21700000
Emergence from stealth with $21.7M
22600000
Raised $22.6M to commercialize chips
100000000+
Series B announced as more than $100M
“Includes both financial and strategic investors such as Tiger Global, Samsung Ventures, CTBC/HH-CTBC, AlleyCorp, and others”
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Title : DFT Architect
Location : Bangalore/Remote(Anywhere in India)
Enchargeai.com
DFT ARCHITECTURE:
MBIST LBIST Logic Design using System Verilog/Verilog.
-Using Siemens/Mentor DFT tools to implement and verify DFT
Architecture/Structures (EDT, LBIST, and SSN ,EDT, MBIST,
IJTAG, 1149.1, 1149.6) in ASICs.
-Run atpg, analyze coverage, use VCS and Questa to simulate
at unit & sdf.
-Perform test insertion for embedded block in SoC.
-Performing general DFT work on SoC.
-Using Cadence tools(modus) to insert/verify DFT logic.
Some tasks in include, running ATPG to verify DFT
implementation is working at block and chip level, perform fault
coverage analysis, root cause low coverage issues, simulate
ATPG and MBIST to verify DFT structure and patterns, work
with PD to close timing on post-layout netlists, create silicon
bring up plan/strategy.
Used Siemens DFT tools to perform test insertion for
embedded block in SoC.
Used Makefile, TCL, Python to perform DFT insertion
Automation.
Contact:
Supriya Sharma
Supriya@mulyatech.com