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A.edu Departments of Electrical and Computer Engineering and Computer Science , University of Virginia Abstract Accelerators are special purpose processors designed to speed up compute-intensive sections of applications. Two extreme endpoints in the spectrum of possible accelerators are FPGAs and GPUs, which can often achieve better performance than CPUs on certain workloads. FPGAs are highly customizable, while GPUs provide massive parallel execution resources and high memory bandwidth.

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FPGA and GPU processors can execute an AI algorithm much more quickly than a CPU. This means that an AI application or neural network will learn and react several times faster on a FPGA or GPU compared to a CPU.

An FPGA (Field Programmable Gate Array) is an integrated circuit consisting of a collection of “logic blocks”, “I/O Cells” and “interconnection resources”; this allows the chip to be reconfigured to connect the inputs & outputs (I/O) and logic blocks together in many different ways.

FPGA as a Service (FaaS) provides instances that are equipped with field-programmable gate arrays (FPGAs). These instances are known as FPGA-accelerated instances. FPGA hardware and acceleration applications can be reconfigured to obtain low-latency hardware and elastic resources.

FPGA is known to be excellent in line-by-line image processing while GPU is optimal in handling big texture frames at a time. GPU can use big intermediate RAM buffers easily, and the excellent interoperation with the main CPU makes it great for complex computation.

FPGAs tend to have more flexible architecture as compared to GPUs. With GPUs, your reprogramming capabilities are restricted as the data flow in these structures is determined by software and is directed by a complex hierarchy of the GPU's internal memory.

FPGAs allow you to process many tasks in parallel. This is especially useful and more efficient when handling large data sets. Sort, filter, and decimate operations are done more efficiently and allow the primary processors to focus on the signal processing algorithms.

Compared with GPUs, FPGAs can deliver superior performance in deep learning applications where low latency is critical. FPGAs can be fine-tuned to balance power efficiency with performance requirements.

FPGAs are power efficient when compared to GPU because FPGAs are hardware implemented while GPUs are historical, and they hog powers. Therefore, FPGAs are power efficient when compared to GPU.

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Adoption
Bankruptcy
Contractors
Divorce
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Employment
Identity Theft
Incorporation
Landlord Tenant
Living Trust
Name Change
Personal Planning
Small Business
Wills & Estates
Packages A-Z
Form Categories
Affidavits
Bankruptcy
Bill of Sale
Corporate - LLC
Divorce
Employment
Identity Theft
Internet Technology
Landlord Tenant
Living Wills
Name Change
Power of Attorney
Real Estate
Small Estates
Wills
All Forms
Forms A-Z
Form Library
Customer Service
Terms of Service
DMCA Policy
About Us
Blog
Affiliates
Contact Us
Privacy Notice
Delete My Account
Site Map
All Forms
Search all Forms
Industries
Forms in Spanish
Localized Forms
Legal Guides
Real Estate Handbook
All Guides
Prepared for You
Notarize
Incorporation services
Our Customers
For Consumers
For Small Business
For Attorneys
Our Sites
US Legal Forms
USLegal
FormsPass
pdfFiller
signNow
airSlate workflows
DocHub
Instapage
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Call us now toll free:
1-877-389-0141
As seen in:
  • USA Today logo picture
  • CBC News logo picture
  • LA Times logo picture
  • The Washington Post logo picture
  • AP logo picture
  • Forbes logo picture
© Copyright 1997-2025
airSlate Legal Forms, Inc.
3720 Flowood Dr, Flowood, Mississippi 39232