The key difference between Harvard and Von Neumann architectures is that Harvard architecture has physically separate storage and signal pathways for instructions and data, while Von Neumann architecture uses the same memory and pathways for both instructions and data. This separation allows Harvard architecture to achieve greater parallelism and throughput for certain workloads.
What is Von Neumann Architecture?
The Von Neumann architecture, named after mathematician and early computer scientist John von Neumann, describes a standard computer design model where a single processing unit performs all data processing operations. This processing unit is connected to a shared memory system that holds both instructions and data.
In Von Neumann architecture, data and instructions are stored in the same memory and travel across the same pathways to the CPU. This creates a bottleneck, as the CPU has to wait for data to come from memory before it can execute the next instruction. Only one operation can take place at a time.
Von Neumann bottlenecks limit the performance of these systems. As CPU speeds have increased, memory speeds have struggled to keep up. Waiting for memory accesses now makes up a significant portion of CPU time in modern Von Neumann machines.
However, the Von Neumann design is conceptually simple and easy to program for. It formed the basis for almost all general purpose computers built in the second half of the 20th century. Even as computer architectures have evolved, Von Neumann principles can be found at their core.
Key Characteristics of Von Neumann Architecture
- Single Processor performs all data processing operations
- Shared Memory holds data and instructions
- Data and Instructions use same pathways
- Bottleneck between CPU and Memory limits performance
- Conceptually simple to understand and program
What is Harvard Architecture?
The Harvard architecture is a computer design model where data and instructions are stored separately, and travel on different data pathways. This segregation allows concurrent access to memory, and enables highly pipelined implementations.
In Harvard architecture, program memory and data memory occupy separate storage devices connected over different data buses. Instructions pass from program memory to the CPU on dedicated instruction pathways. Meanwhile data flows between main memory and the CPU on separate data pathways.
This separation prevents instructions and data from interfering with each other’s performance. The CPU can simultaneously retrieve an instruction, operate on data, and return the results. Multiple operations can take place concurrently with no bottlenecks.
Dedicated data pathways also enable highly optimized implementations tailored for each use case. For example, program memory can be optimized for fast reads, while data memory is optimized for writes.
The downside is that Harvard architecture machines are more complex to program, as data access and management requires more coordination. This makes Harvard more suitable for specialized use cases like embedded systems.
Key Characteristics of Harvard Architecture
- Physically separate storage and data pathways for instructions and data
- Instructions and data can be accessed concurrently
- Avoids Von Neumann bottlenecks
- Enables pipelined implementations
- More complex to coordinate and program
Differences Between Harvard and Von Neumann
The key differences between the Harvard and Von Neumann architectures can be summarized as:
- Memory: Harvard uses separate memories for instructions and data. Von Neumann uses shared memory.
- Pathways: Harvard has independent pathways for instructions and data. Von Neumann uses shared pathways.
- Bottlenecks: Harvard avoids bottlenecks by allowing concurrent access. Von Neumann experiences bottlenecks due to shared resources.
- Performance: Harvard enables greater speed and parallelism. Von Neumann is sequentially limited.
- Programming: Harvard is more complex to program. Von Neumann is conceptually simpler.
The physical separation of memories and pathways in Harvard allows for highly optimized concurrent operations. But it requires more coordination between instruction and data flows. Von Neumann has conceptual simplicity but suffers from inherent bottlenecks.
Examples of Harvard Architecture
Harvard architecture is commonly used in CPU and MCU designs optimized for digital signal processing, graphics processing, and other computationally intensive workloads.
Some examples include:
- ARM Cortex-M – Microcontroller family designed for real-time applications like automotive systems, robotics, IoT devices. Uses Harvard architecture with separate 32-bit program and data buses.
- Intel x86 – Modern x86 CPUs use a modified Harvard architecture. Caches and prefetch buffers provide logical separation between instruction and data flows.
- GPUs – Graphics processing units process huge volumes of visual data parallelly. Harvard architecture enables concurrent access to the different memory types.
- DSPs – Digital signal processors use Harvard variants to enable pipelined execution of signal processing algorithms with optimized data and program memories.
- MicroBlaze – Soft processor core designed by Xilinx for FPGA and embedded systems uses Harvard architecture for instruction and data separation.
Even some early personal computers like the IBM PC used elements of Harvard architecture by separating the CPU’s cache from the external DRAM memory areas.
Examples of Von Neumann Architecture
Some examples of pure or modified Von Neumann architecture include:
- Intel 8086 – The original x86 microprocessor that formed the basis for the entire x86 family uses a classic Von Neumann single bus architecture.
- ARM7 and ARM9 – Early ARM processor cores that powered smartphones and embedded devices featured simplified Von Neumann designs.
- PIC – Microcontrollers like PIC16 and PIC18 from Microchip use a Von Neumann architecture for simple and low-cost embedded systems.
- Zilog Z80 – 8-bit microprocessor introduced in 1976 and used in computers like the Radio Shack TRS-80 was based on pure Von Neumann architecture.
- Intel 4004 – The world’s first commercial microprocessor developed in 1971 implemented a 4-bit Von Neumann design.
While newer processor designs have moved away from pure Von Neumann models, the shared memory and bus pathways remain at the core of general purpose CPU architectures today.
Harvard vs Von Neumann Performance
Harvard architecture delivers significantly higher performance than Von Neumann designs for several reasons:
- Parallelism – Simultaneous instruction and data access enables pipelined execution.
- Speed – No shared bus contention allows faster clock speeds.
- Optimized Memory – Physically separate memories tailored for each use case.
- Prefetching – Instruction prefetch buffers minimize delays.
- Scalability – More cores can be added without shared resource contention.
Benchmarks show Harvard machines significantly outperforming equivalent Von Neumann designs on matrix math, cryptographic workloads, image processing, neural networks and other parallel tasks.
However, pure Harvard architectures can show lower performance on general purpose code with more linear instruction flows. The separate memories also consume more space and power.
Modified Harvard Architectures
Most modern processor designs do not follow pure Harvard or Von Neumann models. Instead they take a modified Harvard approach.
Modified Harvard architectures attempt to achieve the best of both worlds – the programming ease of Von Neumann models with the performance benefits of Harvard.
This is done by using shared memory for both data and instructions, but with separate CPU caches and data pathways that emulate the separate memories of Harvard machines. Instructions and data may have different caching strategies as well.
Prefetch buffers are used to load instructions sequentially from shared memory into the instruction cache/pathway before they are required. This prevents contention with random data accesses to the shared memory space.
Examples include modern x86 and ARM processors. The strict separation between caches allows them to achieve many of the performance benefits of Harvard, while keeping the conceptual simplicity of a shared memory model.
So while pure Harvard architectures are relatively rare in modern systems, the key principles of separated pathways guide the design of most contemporary computing architectures.
Memory Management
Memory management differs significantly between the two architectures:
- Von Neumann – All memory access goes through the same interface and pathways. Virtual addresses are mapped to physical addresses via the MMU.
- Harvard – Separate instruction and data address spaces. Instruction addresses may be virtual while data addresses are physical.
In Von Neumann machines, a Memory Management Unit (MMU) maps program-visible virtual addresses to physical addresses where contents actually reside. This lets programs use a contiguous address space without worrying about physical allocation.
In Harvard machines, instruction addresses are commonly virtualized, while data space uses physical addressing. The instruction MMU maps virtual instruction addresses to physical program memory. This makes programming the instruction flow easier. Data memory can directly use physical addresses.
Caching and translation lookaside buffers (TLBs) are used to accelerate virtual to physical mappings in both architectures.
Cache Implementation
There are also significant cache architecture differences:
- Von Neumann – Unified cache for both instructions and data.
- Harvard – Separate caches for instruction and data.
In Von Neumann machines, a unified cache stores both instructions and data using the same indexing schemes. Care needs to be taken to maintain coherence between the cache and main memory.
In Harvard architectures, separate instruction and data caches are used. The instruction cache is read-only and loads code sequentially before use. The data cache is write-back and optimized for low latency loads/stores. This avoids coherence issues.
Caches in both architectures exploit principles of temporal and spatial locality to optimize performance. But Harvard’s split caches reduce contention and the need for expensive coherency protocols.
Real-World Implementations
Most real-world computer architectures implement a blend of modified Harvard and Von Neumann principles:
- x86 – Uses shared memory address space like Von Neumann. But separate caches/pathways provide logical separation.
- ARM – Has unified shared memory but with separate instruction and data prefetch buffers enabling concurrency.
- GPUs – Use shared addresses but separate instruction, constant and texture caches to enable massive parallelism.
- Network CPUs – Have unified caching but separate packet processing pipelines avoiding packet/instruction contention.
So while very few processor designs follow pure Harvard or Von Neumann models, the principles they established form the bedrock of all modern computer architectures.
Key Takeaways
- Harvard architecture physically separates storage and data pathways, while Von Neumann uses shared resources.
- Harvard avoids bottlenecks and allows greater parallelism and optimization.
- Von Neumann has conceptual simplicity but suffers from inherent contention.
- Most modern CPUs use modified Harvard techniques like separate caches while retaining a shared memory address space.
- Harvard delivers higher performance for parallel workloads like graphics, AI, networking. But has lower single thread performance on general code.
So while neither pure Harvard nor Von Neumann architectures are implemented in modern processors directly, their principles guide the design of everything from simple embedded devices to sophisticated supercomputers. Understanding these fundamental computing models is key to designing optimized next generation architectures.
Harvard architectures enable efficient concurrent data and instruction flows crucial for parallel processing. At the same time, retaining unified memory eases programming complexity and memory management. Striking the right balance between physically separate and logically shared resources holds the key to building even faster and more efficient computing platforms.
