Hardware/software co-design (often shortened as HW/SW co-design) is a system-level design methodology in which hardware and software components are developed together rather than in isolation. Its goal is to improve system-level metrics such as performance, power consumption, cost, and flexibility by trying out different ways of splitting functionality between hardware and software early in the design.
Hardware/Software co-design is widely used in embedded systems, cyber-physical systems, and heterogeneous computing platforms, where software execution and hardware acceleration need to work closely together.
Overview Hardware/software co-design is based on the principle that hardware and software constraints are considered at the same time, so that decisions on one side can be made with the other in mind. A function can be placed either in software on a processor or in dedicated hardware logic, with effects on performance, power, area, and cost at the same time. By keeping both sides open during early design, co-design explores combinations that a one-sided approach would miss.
In co-design, the boundary between hardware and software is treated as a design variable. Computationally heavy, regular work is given a dedicated hardware implementation, while control logic and anything likely to change remains in software. The parameters that define this split, such as bit widths, buffer sizes, and the degree of parallelism, are shared between the two sides rather than fixed independently.
History Hardware/software co-design took shape in the late 1980s and early 1990s, when embedded systems were growing more complex and the usual practice of designing hardware and software separately began to break down. Many early embedded products, such as telecommunications equipment, industrial controllers, and consumer electronics, had tight performance and power budgets that could not be met by tuning the software alone.
Early research looked at how to design digital hardware and embedded software at the same time, so that a given function could be placed either on a processor or in dedicated logic. Researchers developed formal methods for hardware/software partitioning, system-level specification, and performance estimation, and electronic design automation (EDA) tools started to support these combined workflows.
During the 1990s, co-design became closely tied to embedded systems and system on chip design. As processors, memories, peripherals, and custom logic were integrated onto a single chip, designing the hardware and software together became necessary to meet cost, performance, and power targets.
The increasing adoption of field-programmable gate arrays (FPGAs) in the 2000s widened the range of problems co-design could address. Unlike application-specific integrated circuits (ASICs), FPGAs can be reprogrammed after manufacturing, so the hardware can be modified alongside the software. High-level synthesis (HLS) tools lowered the barrier further by letting parts of a program written in high level languages such as C and C++ be turned into digital hardware.
More recently, hardware/software co-design is being adopted for artificial intelligence (AI), cloud computing, and heterogeneous computing. AI accelerators, data center architectures, and domain-specific processors can be built with co-design methods to raise performance-per-watt while keeping flexibility and development cost under control.
Comparison with other methodologies
Hardware/software development methodologies are commonly described as ranging from independent development to fully integrated co-design.
Independent hardware/software design
In the traditional model, hardware and software are developed largely on their own. Hardware engineers build a target platform around architectural and manufacturing constraints, while software developers write applications on the assumption that the hardware interface will not change. The two teams communicate only through predefined instruction sets, programming interfaces, or hardware abstraction layers.
Because the two sides are decoupled, each can proceed at its own pace. This works well for general-purpose computing platforms, desktop software, operating systems, and off-the-shelf hardware, where a single platform has to run a wide range of unrelated programs.
A limitation of this approach is that the separation between hardware and software restricts cross-layer optimization. Software cannot exploit hardware capabilities that were not incorporated into the design, while the hardware cannot be tailored to the behavior of specific applications.
Optimizing software for the hardware
A common approach is to pick or build the hardware first, then tune the software to extract the most performance. The hardware is treated as fixed, and developers adapt their algorithms and code to make the best use of the available resources.
Typical techniques include compiler optimizations, vectorization, cache-aware programming, parallel programming, instruction scheduling, and optimized memory layouts. Additionally, algorithms can be rewritten to fit features such as single instruction, multiple data (SIMD) instructions, graphics processing units (GPUs), digital signal processors (DSPs), or specialized instruction set extensions.
This is a common industrial approach for commercial processors, mainly because redesigning hardware once it is in production is very expensive. As a result, the software has to work within whatever the architecture provides, even when it limits the ideal implementation.
Optimizing hardware for the software
An alternative approach begins with a specific application or workload and then designs hardware to accelerate it. Here the software acts as the specification, and the hardware is built to match. Typical applications include signal processing kernels, neural network inference, cryptographic algorithms, video encoding and decoding, and image processing pipelines.
This approach has long been used in embedded and real-time systems, where low power and resource budgets make custom hardware worthwhile. Building hardware around a known workload can give better performance-per-watt than a general-purpose processor. The same idea has since moved into large-scale cloud computing and data centers, where AI accelerators such as tensor processing units (TPUs) are used to reduce power and raise performance-per-watt.
Hardware/software co-design
Co-design differs from the three approaches above in that neither side is fixed first. Hardware and software are treated as an iterative, collaborative effort, and both are refined together against shared goals. Starting from a system-level specification, alternative hardware/software partitions are evaluated against metrics such as performance, power consumption, area, and cost. The process is typically iterative, with the results of each evaluation guiding subsequent design decisions.
Applications Hardware/software co-design is widely used in systems that combine general-purpose processors with application-specific hardware. Typical application domains include:
Embedded systems, where power consumption, chip area, and manufacturing cost are primary design constraints. Automotive and aerospace, where safety-critical control loops must satisfy real-time requirements. Telecommunications and digital signal processing, where functions such as filtering, modulation, and error correction are commonly implemented in dedicated hardware. Computer vision and image processing, where computationally intensive pixel-processing kernels are often accelerated in hardware. Artificial intelligence accelerators, including TPUs, neural processing units (NPUs), and GPU tensor cores, which are developed together with the software frameworks and numerical formats they support. Data centers and cloud computing, where FPGA- and ASIC-based accelerators are used for networking, storage, database, and machine-learning workloads. Scientific computing, where application-specific accelerators are used for computational kernels such as stencil computations, molecular dynamics, and sparse linear algebra.
Design workflow
A hardware/software co-design flow typically begins with a system-level specification, followed by hardware/software partitioning, design space exploration, and performance estimation. These activities are generally performed iteratively, as the results of one stage often influence decisions made in the others.
Partitioning Partitioning decides, for each function or kernel, whether it runs as software on a processor or as a dedicated hardware block. Kernels built from regular, data-parallel arithmetic, such as convolution, matrix multiplication, or fast Fourier transform (FFT) stages, are good candidates for hardware. Control-heavy code, rare operations, and anything expected to change over the product's life are typically left in software.
Partitioning is normally treated as a multi-objective optimization problem that trades off execution time, power, silicon area, memory bandwidth, and development effort. Reported approaches include integer linear programming, graph partitioning, evolutionary algorithms, simulated annealing, and more recently machine learning heuristics.
Design space exploration The number of possible partitions and hardware configurations grows very quickly, so design space exploration (DSE) is used to narrow things down. DSE tools vary parameters such as accelerator count, memory hierarchy, interconnect topology, and clock frequency, then score analyzed configurations against the system goals.
These scores can be computed through analytical models, instruction-set simulators, transaction-level SystemC models, and FPGA prototypes. There is a trade-off between accuracy and speed: rough analytical models can evaluate thousands of options quickly, while slow but accurate cycle-level simulation and FPGA prototyping are saved for a short list of promising designs.
+ Evaluation methods used in design space exploration Method Relative speed Relative accuracy Typical use Analytical models Very fast Low Early screening of many configurations Instruction-set simulation Medium Medium Evaluation of candidate processors Cycle-accurate or SystemC Slow High Detailed evaluation of selected configurations FPGA prototyping Slow to set up, fast to run Very high Final validation
Co-design choices Beyond the basic "hardware or software" question, co-design involves a set of joint choices that are relevant to both hardware and software. Many of them rely on operations that are cheaper in hardware than in software, such as shifts and masks, bit-level parallelism, and arithmetic with a fixed, known latency.
+ Common co-design choices Choice Hardware effect Software implications Reduced precision Reduced arithmetic cost and memory bandwidth Numerical algorithms adapted to reduced precision (like quantization-aware training for AI) Loop tiling Improved locality through on-chip memory reuse Loop structure and data access patterns matched to the hardware organization Processing-element count Degree of hardware parallelism Parallelism exposed through vectorization, threading, or loop transformations Fixed versus programmable Trade-off between efficiency and flexibility Flexibility to accommodate evolving algorithms and workloads Data layout Efficient memory access and accelerator interfaces Data structures organized to match the hardware memory layout
Numerical precision and quantization