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AI Hardware-Software Co-Design: Optimizing Performance Together
Key Takeaways
Designing AI chips and software together recovers performance and energy that general-purpose hardware wastes, but chip-level co-design pays off mainly for large, stable workloads.
- General-purpose CPUs and GPUs handle AI's irregular patterns and heavy matrix multiplication inefficiently, and large language models leave their compute and memory bandwidth underused.
- Co-design works through five levers: custom accelerators, memory hierarchy, power efficiency, parallelism, and the compiler and framework layers between a model and the chip.
- Google's TPU code is compiled through XLA, and AWS says first-generation Inferentia delivers up to 2.3x higher throughput and up to 70% lower cost per inference than comparable EC2 instances.
- Vendor "up to" figures depend on the workload, and the TPU's well-known efficiency result compared the first-generation TPU against hardware of its era, not current GPUs.
- Most companies apply co-design at the compiler and SDK layer, by choosing an accelerator whose toolchain fits their framework, without building a chip.
- Each vendor toolchain, such as XLA for TPUs and Neuron for Inferentia, adds porting work and some lock-in.
Profile current training and inference workloads first to see where time and power go.
AI compute demand is outrunning general-purpose hardware. Epoch AI's trends data shows that training compute for frontier language models has grown about 5x per year since 2020, a rate that chips designed for broad workloads can't match on their own.
AI hardware-software co-design means building the chip and the software stack together. Each is shaped to fit the other. Fitting them together recovers the performance and energy that independent design leaves unused. Below, you'll find what co-design aims for, the levers it uses, how Google's TPU and Amazon's Inferentia apply it, and when it's worth the effort.
What is AI Hardware-Software Co-Design?
AI hardware-software co-design is the collaborative design of hardware and software systems to improve AI workloads. Traditionally, teams develop and optimize the two separately. Co-design adjusts both at the same time, in repeated iterations, so the resulting infrastructure is tailored to specific AI models or workloads, which improves performance and cuts energy use.
What Does AI Hardware-Software Co-Design Aim to Achieve?
Co-design targets four outcomes.
- Better performance, meaning higher throughput and lower latency for training and inference.
- Energy efficiency, which cuts power consumption in large-scale training and inference.
- Scalability, so efficiency holds as models and datasets grow.
- Customization, with hardware and software fitted to the needs of a specific AI application.
Why Traditional Design Approaches Fall Short
In the traditional design flow, software is often written for general-purpose hardware such as CPUs or GPUs, which is convenient but inefficient for AI, especially as models grow more specialized and demanding. Today's large language models are a clear case. They need a lot of compute and memory bandwidth, and that hardware leaves both underused.
AI models often involve irregular computational patterns and intensive matrix multiplications, which general-purpose processors aren't always equipped to manage efficiently. The result is bottlenecks in speed and performance.
Hardware-software co-design responds by customizing hardware elements, such as accelerators and memory hierarchies, for AI algorithms. Pair that hardware with specialized AI development services that fine-tune software to match its capabilities, and both execution time and energy use can drop.
How Co-Design Optimizes AI Hardware and Software
Co-design works through five levers, which are custom accelerators, the memory hierarchy, power efficiency, parallelism, and the compiler and framework layers that sit between a model and the chip beneath it.
Custom AI Accelerators
Google's Tensor Processing Units (TPUs) are custom AI accelerators produced through co-design. They're tuned for deep learning operations. On those operations they run much faster than general-purpose processors, and the software on top can then be optimized to use the parallelism and specialized instructions each chip offers.
Memory Hierarchy Optimization
Memory management directly affects AI speed. AI tasks move a great deal of data back and forth between levels of memory, and with co-design, developers can design hierarchical memory systems that improve data locality and reduce latency. Training, for example, relies on fast memory to cut the delays caused by data movement.
Power Efficiency in Training and Inference
Balancing performance against power consumption is part of co-design, and it matters most in large-scale deployments. Algorithms are shaped to suit energy-efficient hardware. Continuous automation testing services can then check performance and power use after each model or code change, so a regression in energy demand is caught before it reaches production.
Parallelism and Pipelines
AI algorithms benefit from running many computations at once. With co-design, the hardware is made for parallel execution and the software is pipelined to keep those parallel units busy, which raises throughput.
Compilers and Framework Layers
Compilers and SDKs are part of the design. Programs for Google TPUs are compiled through the XLA (accelerated linear algebra) compiler, and on the AWS side, the Neuron SDK connects Inferentia to frameworks such as PyTorch and TensorFlow. This layer is where most teams can apply co-design without building a chip.
Google TPU and Amazon Inferentia as Co-Design Examples
Google and Amazon each built a chip for AI at cloud scale and shipped a software layer designed to fit it.
Google TPU
Google's TPUs are custom chips for the large matrix operations at the center of neural networks. Google designed the software stack with them, and TPU code is compiled through XLA. As of October 2026, Cloud TPUs support JAX, PyTorch and TensorFlow, including TorchTPU, a native PyTorch backend built with the PyTorch team.
The best-known efficiency result is historical. Google's 2017 ISCA paper, "In-Datacenter Performance Analysis of a Tensor Processing Unit", compared the first-generation TPU, an inference-only chip, with a server-class Intel Haswell CPU and an Nvidia K80 GPU and reported large gains in performance per watt. Those comparisons were made against hardware of the same era, not current GPUs.
Amazon Inferentia
Amazon's Inferentia chips handle AI inference in AWS. On its Inferentia product page, AWS states that first-generation Inferentia delivers up to 2.3x higher throughput and up to 70% lower cost per inference than comparable Amazon EC2 instances. Its software side is the AWS Neuron SDK. As of October 2026, Neuron integrates natively with PyTorch and TensorFlow, so developers can stay in the frameworks they already use.
Porting Effort and Lock-In with Vendor Chips
Moving a model between platforms costs porting work and brings some lock-in, because each of these chips ties code to a particular compiler or SDK, such as XLA for TPUs and Neuron for Inferentia. Vendor "up to" figures also depend on the workload. Treat them as an upper bound and test against your own models.
The Future of AI Hardware-Software Co-Design
Research on new kinds of hardware points to where co-design may go next, though these systems are still experimental. Intel's Hala Point neuromorphic system is a research prototype meant to advance future commercial systems, and early results reached deep neural network efficiencies as high as 15 TOPS/W as of April 2024.
Quantum computing is the other example. In 2023, Google Quantum AI showed, for the first time, that increasing the size of a surface code decreases the logical qubit's error rate. Both behave differently from CPUs and GPUs, so they need software designed specifically around them.
Most companies will never build a chip. They'll meet co-design through the compilers and SDKs that ship with accelerators, and through AI solutions whose models and code are tuned for the cloud hardware they rent.
When AI Hardware-Software Co-Design Is Worth Pursuing
Chip-level co-design suits only large, stable workloads. Our view is that custom silicon pays off for companies like Google, which run the same kinds of models at that scale. Teams with smaller or changing workloads get most of the benefit by choosing an accelerator whose compiler and SDK fit their framework, then optimizing their models and software for it. The catch is porting effort, since each platform's toolchain adds work and some lock-in.
Start by profiling your current training and inference workloads to see where time and power go. With that data in hand, Arbisoft's AI engineering team can help match your models to the right hardware through our AI development services.
Frequently Asked Questions About AI Hardware-Software Co-Design
How is hardware-software co-design different from just using GPUs?
With general-purpose GPUs, you adapt software to hardware that was made for a broad range of tasks, while co-design shapes the chip, the compiler and the models together. Google's TPUs are an example. TPU code is compiled through XLA, so the chip and its compiler work as one design.
Is AI hardware-software co-design only for big tech companies?
No, though building custom chips usually is. Smaller teams apply co-design at the software layer by picking an accelerator that suits their workload and optimizing code for its compiler or SDK. AWS's Neuron SDK is one route, since it works with PyTorch and TensorFlow and lets you tune the models you already have for Inferentia without designing any hardware of your own.
Can existing PyTorch models run on TPUs or Inferentia?
Yes, both platforms support PyTorch. Cloud TPUs support JAX, PyTorch and TensorFlow as of October 2026, and TorchTPU, built with the PyTorch team, provides a native backend. On Inferentia, AWS Neuron supports both PyTorch and TensorFlow, although some porting work may still be needed on either platform, especially for custom operations or code written around a specific GPU.
Does co-design reduce AI energy use?
It can. Hardware specialized for a workload does more useful work per watt, and neuromorphic research points to how far that can go. In early results, Intel's Hala Point, a research prototype, reported deep neural network efficiencies as high as 15 TOPS/W. Actual savings depend on the workload and on how well the software fits the hardware.
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