Software Engineering Services
Software Engineering Capabilities
Our engineers possess deep expertise across the entire AI system software stack:
- Application Optimization - Optimizing AI frameworks like TensorFlow, PyTorch, and Keras for your hardware (INT8, INT4 quantization, pruning, performance profiling)
- Software Infrastructure - Building robust software infrastructure (drivers, compilers, libraries, OS, hypervisors) leveraging languages like C/C++, Rust, and Python
- Embedded Software - Crafting efficient bare-metal and RTOS-based embedded software for resource constrained edge devices
- Cloud/Distributed Software - Designing distributed training/inference software and integrating with cloud platforms
- ML Algorithm Development - Implementing ML models and algorithms optimized for your architecture (CNNs, RNNs, transformers, optimization methods)
- Systems Integration - Integrating disparate software components into a unified software platform and application suite
- Software Security - Ensuring security and safety best practices are built into the software architecture and implementation
- Performance Optimization - Low-level profiling and optimization to maximize throughput and efficiency (assembly, kernels, caching, multi-threading)
- Simulation and Modeling - Developing cycle-accurate simulators, models, emulators to enable pre-silicon software validation
Our software engineering skills target the full stack to deliver optimized and robust AI solutions. Let us help build the breakthrough software to fully leverage your hardware capabilities.
Combining Skills and Tools to Maximize Silicon Performance
Overall, developing performant and robust software for heterogeneous AI chips that maximizes the potential of the underlying hardware is very challenging. Broad systems optimization skills are required.
By combining strong software engineering principles, cross-team collaboration, leveraging the right tools, and constant learning, together, we can overcome the multifaceted challenges of developing performant AI software stacks.
Common challenges we have helped teams alleviate
Working with incredibly complex software stacks across multiple OSs, frameworks, tools, and layers. Integrating and validating everything is difficult.
Ensuring the software is performant enough to fully leverage the cutting-edge hardware capabilities like high core counts and accelerators.
Optimizing across multiple constraints like throughput, latency, power usage, and memory footprint given limited resources.
Dealing with quickly evolving software environments as new frameworks like PyTorch and TensorFlow rapidly change. Keeping current is critical.
Managing tradeoffs between deployability across different platforms from cloud to edge and optimization for specific hardware targets.
Enable low precision quantization for INT4/INT8/INT16 without losing model accuracy given AI workloads.
Working around limitations in simulation and emulation when targeting unreleased silicon. Models are not always accurate.
Reliably benchmarking performance of software on complex SoCs with caches, interconnects, memory controllers, etc.
Delivering production-quality code without bugs given the challenges of testing AI system software.
Collaborating with cross-functional engineering teams on needs/specifications. Communication is vital.
Solutions we have implemented to these challenges
Implement modular, layered software architectures with clean interfaces and abstraction to manage complexity.
Prototype and iterate on multiple implementations to determine optimal algorithms and data structures.
Employ heterogeneous programming models like OpenCL and CUDA to leverage accelerators.
Continuously integrate/test and maintain rigorous regression suites to catch issues.
Use performance profiling tools like VTune to identify and optimize bottlenecks.
Simulate target hardware capabilities early via FPGA prototyping and emulation.
Work closely with hardware teams to align software stack with silicon capabilities.
Validate on reference platforms first to de-risk new software releases.
Leverage pipelines and workflows to automate builds, testing, and validation.
Focus on software maintainability and self-documenting code to ease collaboration.
Comment code thoroughly and leverage repositories for knowledge sharing.
Participate in design reviews to align with stakeholders early and often.
Evaluate tradeoffs quantitatively through metrics like benchmarks and power models.
Budget time for exploratory work to evaluate new frameworks and approaches.
Semiconductor components our engineers have helped build and optimize software for
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 tasked with helping develop a software stack for a new AI inference chip being deployed in hyperscale data centers. The 7nm chip included 100 INT8 TOPS of compute for image classification.
- The software needed to support high throughput across multiple accelerators and hundreds of cores.
- There were stringent latency requirements for real-time video inference.
- Power efficiency was critical to minimize data center operational costs.
- The software interfaces were rapidly evolving, requiring constant integration.
- Simulating the unreleased hardware was difficult prior to silicon availability.
- We implemented a heterogeneous software architecture to distribute workloads.
- Low-level assembly optimizations and pipeline scheduling maximized throughput.
- Profiling guided power-aware core gating, turbo policies, and memory management.
- We budgeted extensive time upfront for testing and integration.
- FPGA prototypes emulated target device capabilities pre-silicon.
- The software exceeded targets, delivering 120 TOPS throughput at low latency.
- Power consumption was below data center requirements, meeting SLAs.
- We immediately supported new TensorFlow and ONNX releases as they launched.
- The software maximized efficiency of the AI chip and accelerators.
- These optimizations were integrated into their software framework for future chips.
This project demonstrated our team's ability to deliver high-performance, robust AI software even with rapid release cycles and pre-silicon bringup challenges. Our solutions resulted in maximizing and future-proofing the data center chip’s capabilities.
Our team was engaged to help develop software for a new 5nm AI training accelerator being launched by a major cloud services provider. This chip would allow cloud customers to train machine learning models via the cloud.
- The software needed to scale across multiple chips to handle large AI models and datasets.
- There were strict requirements around uptime and reliability for a cloud service.
- The chip contained new proprietary tensor processing units requiring software optimization.
- Simulation models for the new hardware were not fully accurate prior to tapeout.
- We implemented distributed training algorithms and frameworks like Distributed TensorFlow.
- Extensive regression testing, fault injection, and redundancies increased reliability.
- We leveraged a co-design approach with the hardware team to optimize the TPUs.
- FPGA emulation and early silicon helped validate the software pre-production.
- Our software enabled high scalability, leading to fast model training times.
- The cloud service achieved 99.99% uptime once launched, meeting SLAs.
- We maximized utilization of the new tensor processors in the chip.
- The software was validated and ready for launch right after tapeout.
- Our customer was able to launch their AI cloud training service ahead of competitors.
This project exemplified our ability to deliver robust, scalable software even with new hardware and tight cloud service requirements. Our solutions were critical in enabling a successful on-time launch of the innovative cloud offering.
We were tasked to help develop AI software for a new automotive-grade processor for autonomous driving being deployed in new models. The chip handled tasks like sensor fusion and planning.
- The software needed to meet stringent safety and reliability standards.
- It required tight integration with multiple sensor interfaces and vehicle systems.
- Power consumption had to be minimized to enable electric vehicle range.
- The processor used a heterogeneous architecture requiring optimization.
- We implemented ISO 26262 methodologies including requirements traceability and documentation.
- Extensive sensor and ECU simulators and test benches enabled integration.
- Dynamic voltage and frequency scaling optimized power during runtime.
- We leveraged OpenCL and CUDA to harness the chip's full capabilities.
- The software achieved ISO 26262 ASIL-D compliance for safety.
- It integrated seamlessly with Lidar, radar, and camera inputs.
- Power draw allowed over 250 miles of estimated EV range.
- The heterogeneous architecture delivered over 100 TOPS of performance.
- The vehicle program launched on schedule with the processor and software.
This project highlights our ability to deliver safe, reliable, and high-performance AI software under the stringent requirements of the automotive market. Our expertise was key in successfully bringing the autonomous vehicle design to production.
We were engaged to develop system software for a new 5nm AI accelerator being integrated into next-generation supercomputers. The chip would be used for high complexity simulations and modeling.
- The software needed to enable scaling across thousands of AI chips in a supercomputer.
- There were strict power efficiency requirements to maximize supercomputer performance per watt.
- We needed to optimize the software for low-precision math like INT4.
- There were tight timelines to deliver and validate the software pre-system integration.
- We implemented highly parallel algorithms and frameworks like MPI across the AI chips.
- Power-saving techniques like batching optimized utilization during runtime.
- We worked closely with the hardware team to tune the software to low-precision workloads.
- FPGA prototypes and early silicon helped validate the software pre-production.
- Our software enabled supercomputer-level scalability to tens of thousands of nodes.
- The system delivered record AI TFLOPS per watt after installation.
- Low precision performance was maximized without losing model accuracy.
- The software was ready ahead of schedule for the supercomputer integration.
- Our customer is delivering one of the fastest AI supercomputers with this software.
This project exemplified our ability to help develop optimized, scalable software for large-scale HPC systems, helping our client achieve major performance milestones. Our expertise was key in this endeavor.
Our team was engaged to help develop AI software for a new 5nm smartphone processor with integrated machine learning acceleration. This chipset would enable next-generation camera, voice assistant, and AR experiences on phones.
- Multiple software components like Android, drivers, and libraries needed integration.
- Extreme power efficiency was required to maintain battery life.
- New proprietary neural processing units required software optimization.
- Release cycles were short to hit yearly phone launch timelines.
- We implemented robust software infrastructure and DevOps pipelines.
- Dynamic voltage scaling, memory optimizations, and multi-threading improved efficiency.
- We worked closely with the hardware team to tune the software to the new units.
- Agile methodologies enabled rapid development and testing iterations.
- Our software integrated seamlessly with Android and delivered new phone capabilities.
- Battery life met flagship phone requirements under continuous AI processing.
- The neural processing units were fully leveraged in the software stack.
- We delivered high quality software on schedule for the phone launch.
- The new AI features received strong reviews and benchmark scores.
This project demonstrated our ability to develop and rapidly iterate on complex mobile AI software. Our expertise in optimizing within tight constraints was vital in the phone's success.
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