The deployment of hardware accelerators significantly enhances the computational power of a system by offloading specific, computationally intensive tasks from the general-purpose CPU to specialized hardware units designed for those tasks. This leads to several key effects on system performance and efficiency:
**1. Increased Performance Through Parallelism and Specialization
Accelerators are engineered to execute particular operations much faster than CPUs by exploiting parallelism and specialized circuits. For example, hardware cryptographic accelerators can process many cryptographic operations simultaneously, completing tasks far more quickly than a CPU that handles them sequentially. This specialization allows accelerators to deliver dramatic speedups for their target workloads, often improving performance by orders of magnitude compared to CPU-only execution[8][5][7].
**2. Improved Energy Efficiency
Contrary to the traditional belief that adding hardware increases power consumption, carefully designed accelerators can reduce overall system power. This is because accelerators can perform operations more efficiently, requiring fewer clock cycles and allowing the system to run at lower clock frequencies while maintaining or improving performance. For instance, adding accelerators to an embedded system reduced execution cycles nearly 90-fold and cut power consumption significantly, sometimes to less than one-fifth of the CPU-alone power, by enabling lower operating frequencies and more efficient computation[5].
**3. Offloading CPU Workload and Enabling More Complex Applications
By handling specialized tasks such as cryptographic processing, matrix multiplication, or machine learning inference, accelerators free up the CPU to focus on other system functions. This offloading not only boosts overall throughput but also enables the integration of more advanced features and complex applications without overburdening the main processor[8].
**4. Flexibility and Adaptability in System Design
Some accelerators, like FPGAs, offer both high computational power and energy efficiency, making them suitable for flexible acceleration tasks at the edge of networks. Deploying accelerators allows systems to be tailored for specific workloads, balancing performance, power, and cost constraints effectively[4][5].
**5. Challenges and System-Level Management
The heterogeneity introduced by accelerators requires careful system and operating system support to allocate resources efficiently and schedule tasks. Proper management ensures that accelerators are utilized optimally, maximizing their performance benefits while maintaining system stability and power efficiency[7].
**6. Reduction of Data Movement and Communication Overheads
In accelerators designed for tasks like matrix multiplication, on-chip data reuse and efficient buffering reduce the need for frequent data transfers between memory and processing elements, minimizing bandwidth bottlenecks and energy costs associated with data movement[10].
In summary, deploying accelerators enhances a systemâs computational power by enabling faster, more energy-efficient execution of specialized tasks, freeing CPU resources, and allowing for more complex and demanding workloads. This results in significant performance gains and power savings, especially important in embedded, edge, and high-performance computing environments[4][5][7][8][10].
Citations:
[1] https://www.ultralytics.com/blog/understanding-the-impact-of-compute-power-on-ai-innovations
[2] https://premioinc.com/blogs/blog/performance-accelerators-in-the-context-of-computing-hardware
[3] http://www.dre.vanderbilt.edu/~gokhale/WWW/papers/HotEdge20_HWAccelReco.pdf
[4] https://www.sciencedirect.com/science/article/abs/pii/S006524582300075X
[5] https://cdrdv2-public.intel.com/650470/wp-01112-hw-reduce-power.pdf
[6] https://www.usenix.org/system/files/osdi24-ma-jiacheng.pdf
[7] https://scail.cs.wisc.edu/papers/hotpar12_rinnegan.pdf
[8] https://www.appviewx.com/blogs/hardware-cryptographic-accelerators-to-enhance-security-without-slowing-down/
[9] https://publications.ics.forth.gr/tech-reports/2018/2018.TR473_Accelerator_Deployment_Models_Heterogeneous_Processing.pdf
[10] https://pmc.ncbi.nlm.nih.gov/articles/PMC11767631/