Design Verification Services
Design Verification Capabilities
Our seasoned verification engineers and architects bring expertise across the entire validation spectrum:
- UVM Environment Development - We architect reusable, configurable UVM testbenches maximizing productivity.
- Coverage-Driven Verification - Leveraging metrics-driven coverage techniques like code coverage, functional coverage, and assertions.
- Testbench Development - Crafting targeted testbenches utilizing SystemVerilog, e, C/C++, and more to validate functionality.
- Simulation Acceleration - Employing techniques like regression test prioritization, parallel simulation, and hardware-assisted validation to speed up verification.
- Emulation - Validating complex designs pre-silicon using emulation platforms like ZeBu, Veloce, and Palladium for earlier software development.
- Formal Verification - Utilizing formal methods like model checking, equivalence checking, and property checking to exhaustively prove key functionality.
- Firmware Driver Verification - Testing and validating firmware driver integration with hardware early in development.
- Post-Silicon Validation - Performing testing on early silicon samples to provide rapid feedback before final production.
- Verification Process Consulting - Advising clients on structured verification plans, methodologies, metrics, and documentation for smooth signoff.
Our expertise in simulating real-world scenarios, achieving comprehensive coverage, and accelerating validation enables our clients to release designs with the highest quality and confidence.
End-to-End Skills for Comprehensive Verification
Functional verification of complex AI SoCs requires a broad range of specialized expertise to comprehensively validate functionality pre-silicon. Methodology is key to an efficient process.
By combining robust verification architecture, intelligent automation, strategic simulation platforms, and comprehensive metrics analysis, We can overcome the multifaceted challenges to achieve thorough pre-silicon validation of complex AI designs.
Common challenges we have helped teams alleviate
Verifying complex system-on-chip designs with billions of possible states and scenarios. Ensuring full coverage is very difficult.
Developing smart stimulus to thoroughly exercise the design in a reasonable time. Stimulus generation is a major effort.
Handling verification complexity at advanced process nodes like 3nm/5nm/7nm with increased defects and variation.
Architecting reusable verification environments to maximize productivity across project teams.
Managing large verification dataset storage and crunching TBs of simulation results data.
Achieving sufficient performance speed in simulation and emulation to model AI workloads.
Developing benchmarks and models to verify specialized AI compute architectures.
Reducing lengthy regression run times to keep up with rapid design changes.
Coordinating with designers and architects to ensure verification aligns with the evolving design.
Maximizing verification reuse across projects without being too design-specific or generic.
Solutions we have implemented to these challenges
Implement layered testbenches with reusable stimulus generators, monitors, and checkers.
Utilize advanced verification techniques like UVM, coverage-driven verification, and Portable Stimulus.
Build automated test generators to efficiently create targeted stimulus.
Leverage emulation platforms for faster simulation and earlier software development.
Create modular verification environments and test suites that can be reused across projects.
Use regression automation tools like Jenkins to speed up simulation and continuous integration.
Develop intelligent testbench analytics to focus verification on coverage holes.
Work closely with designers early on to agree on verification requirements and metrics.
Maintain comprehensive verification plans and documentation for smooth handoff.
Utilize hardware-assisted verification with FPGAs to validate key IP blocks.
Integrate simulation acceleration and hardware emulation for the right tradeoff of speed and accuracy.
Create targeted microbenchmarks focused on specific AI compute subsystems.
Perform direct silicon validation to provide critical post-silicon feedback.
Semiconductor Components We Help Teams Design
Companies like Graphcore, Cerebras, and SambaNova are developing dedicated AI chips optimized for neural network workloads. These feature ultra-high parallelism and memory bandwidth.
Chips designed to accelerate specific parts of AI workloads, like tensor processing units (TPUs) from Google, Intel Spring Crest, and Habana accelerators from Intel. These attach to CPUs or GPUs.
Specialized high-bandwidth memory technologies reduce bottlenecks for AI chips. Examples are HBM from Samsung and MCDRAM from Intel.
These try to mimic the way neurons work through architecures like spiking neural networks. Examples are Intel's Loihi and research chips from IBM.
Field programmable gate arrays tuned for AI workloads by adding blocks for convolution, matrix math, and other operations. Xilinx, Intel, and others offer these.
Using light instead of electricity for chip connections enables high throughput at low power. Intel and others are researching this.
Successful Projects and Case Studies
Our team was responsible for verification of a new 7nm AI training accelerator chip being deployed in data centers. The chip contained a matrix of 512 INT4 cores and integrated HBM2E memory.
- The design complexity meant verification teams were siloed, limiting reuse.
- Stimulating the array of compute cores was manually intensive.
- Meeting aggressive performance targets stretched our simulation capabilities.
- Verifying the complex HBM2E controller thoroughly was time-consuming.
- We developed a unified UVM testbench architecture for all teams to maximize reuse.
- Random stimulus generators and coverage goals automated core testing.
- Emulation platforms provided a 40x speedup over simulation for performance validation.
- We created a configurable HBM2E verification environment handling all protocol tests.
- Our reusable testbench architecture reduced verification effort by 30%.
- Random stimulus improved compute core coverage from 68% to 92% faster.
- The chip met all speed targets proven out on emulation.
- The HBM2E environment simplified memory controller validation.
- Post-silicon, the chip's verification quality exceeded all metrics.
This project demonstrated our competency verifying large-scale AI systems, improving reuse, utilizing advanced tools, and ultimately reducing overall validation time. Our approach set the standard for future data center projects.
We were engaged to verify the design of a new 5nm AI inference chip being deployed for cloud infrastructure. The chip would provide inference-as-a-service featuring integrated neural network accelerators.
- The design contained over 50 different power domains needing verification.
- We had to maximize verification reuse across multiple cloud chip projects.
- Stimulus required cloud-specific scenarios like variable inference batch sizes.
- Stability and reliability were crucial for a cloud service provider.
- An automated UVM environment verified power management across domains.
- We developed a cloud inference testbench framework reusable across projects.
- Cloud usage models guided our intelligent stimulus generation.
- We used formal methods to prove stability for critical subsystems.
- Extensive constrained random stress tests validated reliability.
- Our power verification environment reduced regression time by 20%.
- The reusable cloud testbench accelerated verification for future chips.
- Stimulus effectively covered a wide range of cloud-based scenarios.
- Formal analysis eliminated bugs in key interfaces and control logic.
- Stringent random stability tests validated the design prior to tapeout.
This project demonstrated our specialized expertise verifying AI systems for cloud applications, while maximizing reuse across projects. Our methods played a key role in the successful pre-silicon validation.
We were engaged to verify the design of a new automotive grade AI accelerator chip being deployed in autonomous vehicles. The chip would handle tasks like sensor fusion and driving decision making.
- The design required ISO 26262 compliance for safety and reliability.
- Sensor interfaces like LiDAR, radar, and camera needed verification.
- Modeling the real-time autonomous driving environment was difficult.
- Hardware defects could be life-threatening and required rigorous validation.
- We followed stringent ISO 26262 documentation, traceability, and requirements processes.
- Sensor interface verification involved real-world traffic scenario simulation.
- We leveraged FPGA prototyping to model ADAS scenarios.
- Extensive fault injection, boundary tests, and formal analysis validated the design.
- Our ISO 26262-compliant verification process achieved full certification.
- Interfaces were validated exhaustively against complex simulated inputs.
- FPGA prototyping enabled effective ADAS scenario testing pre-silicon.
- Comprehensive defect analysis ensured the design met safety targets.
- Post-silicon, the chip performed reliably under rigorous real-world conditions.
This project demonstrated our ability to verify an AI chip design within the strict safety and reliability constraints of the automotive market. Our solutions were critical in validating this life-critical system.
We were chosen to verify the design of a new 5nm AI accelerator chip being integrated into next-generation supercomputers. The chip would be used to accelerate complex scientific workloads.
- The chip complexity required verifying thousands of compute elements.
- Modeling real-world HPC applications like physics simulations was difficult.
- We needed to validate extreme low precision INT4 operation.
- Meeting project timeline to integrate with the supercomputer build was critical.
- Constrained random techniques efficiently exercised the massively parallel architecture.
- We developed HPC application microbenchmarks focused on math-intensive algorithms.
- Low precision arithmetic models were integrated into our testbenches.
- Reusable environments and automation maximized verification productivity.
- Regression prioritization ensured completion of critical tests pre-tapeout.
- Our focused stimulus achieved 99% functional coverage of compute resources.
- The microbenchmarks accurately modeled key supercomputing workloads.
- We validated full-chip INT4 performance prior to tapeout.
- Reuse and automation enabled signoff 3 weeks ahead of schedule.
- Post-launch, the chip delivered major performance gains for HPC customers.
This project demonstrated our expertise verifying and optimizing the validation of highly complex HPC-focused AI accelerators. Our techniques were pivotal in the 5nm chip's successful pre-silicon verification and integration.
We were tasked to help verify the design of a new 5G smartphone SoC with integrated AI acceleration. The chip would provide substantial improvements to camera, AR, voice assistants, and more.
- Verifying complex AI inferencing engines for vision, speech, and language.
- Validating the chip met stringent power and thermal requirements.
- Rigorously testing 5G, WiFi, Bluetooth for high reliability.
- Ensuring smooth Android OS integration.
- Meeting demanding project timeline for yearly phone launch.
- Created reusable mobile AI testbench components for various apps.
- Power verification environment modeled real-world smartphone use cases.
- Protocol compliance tests fully validated connectivity interfaces.
- Android Virtual Devices validated key driver integration.
- CI automation and test parallelization accelerated verification.
- Our reusable mobile AI environments reduced testbench development by 30%.
- Power and thermal testing fully covered worst-case smartphone scenarios.
- Network interfaces passed all certification test suites prior to tapeout.
- Android functionality and compatibility was excellent post-launch.
- Chip verified ahead of schedule to meet phone release timeline.
This project exemplified our expertise verifying complex mobile SoCs under rigorous power, performance, and reliability constraints. Our methodology was key to timely validation and integration for the successful smartphone release.
"TeamUP has been our preferred firm to work with to fill our specialized engineering needs. TeamUP has consistently provided us with very experienced and highly qualified candidates to complement our experienced full-time staff. We now use TeamUP as our main agency for our engineering needs and I can highly recommend their service"
"If I look at the world, you’ve got a thousand software engineers, you’ve got a hundred silicon hardware engineers, and then you have one or two CPU development engineers, scale wise, and I've been working with four or five other suppliers specifically trying to find CPU development skills. TeamUP was the only one in the last four months that provided engineers that have actually worked inside a CPU with development experience. They have been able to get me the contractors I need."
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"Their proficiency in advanced verification techniques and their understanding of memory subsystem architecture were impressive"
"The advanced mixed-signal verification expertise and rigorous methodology TeamUP's DV team brought to our Kuiper chip project was timely. Their skill in implementing complex testbenches and driving verification closure played an integral role in our successful pre-tapeout validation and upcoming launch."
"Their expertise in UVM, coverage-driven verification, and testbench automation were instrumental in validating this complex AI chip on an aggressive schedule."