FPGA vs. GPU: Which is better for deep learning?

Underpinning
most
artificial
intelligence
(AI)

deep
learning
is
a
subset
of

machine
learning
that
uses
multi-layered

neural
networks
to
simulate
the
complex
decision-making
power
of
the
human
brain.
Beyond

artificial
intelligence
(AI),
deep
learning
drives
many
applications
that
improve

automation,
including
everyday
products
and
services
like
digital
assistants,
voice-enabled
consumer
electronics,
credit
card
fraud
detection
and
more.
It
is
primarily
used
for
tasks
like
speech
recognition,
image
processing
and
complex
decision-making,
where
it
can
“read”
and
process
a
large
amount
of
data
to
perform
complex
computations
efficiently.

Deep
learning
requires
a
tremendous
amount
of
computing
power.
Typically,
high-performance

graphics
processing
units
(GPUs)
are
ideal
because
they
can
handle
a
large
volume
of
calculations
in
multiple
cores
with
copious
memory
available.
However,
managing
multiple
GPUs
on-premises
can
create
a
large
demand
on
internal
resources
and
be
incredibly
costly
to
scale.
Alternatively,

field
programmable
gate
arrays
(FPGAs)
offer
a
versatile
solution
that,
while
also
potentially
costly,
provide
both
adequate
performance
as
well
as
reprogrammable
flexibility
for
emerging
applications. 

FPGAs
vs.
GPUs

The
choice
of
hardware
significantly
influences
the
efficiency,
speed
and
scalability
of
deep
learning
applications.
While
designing
a
deep
learning
system,
it
is
important
to
weigh
operational
demands,
budgets
and
goals
in
choosing
between
a
GPU
and
a
FPGA.
Considering
circuitry,
both
GPUs
and
FPGAs
make
effective

central
processing
units
(CPUs),
with
many
available
options
from
manufacturers
like
NVIDIA
or
Xilinx
designed
for
compatibility
with
modern
Peripheral
Component
Interconnect
Express
(PCIe)
standards.

When
comparing
frameworks
for
hardware
design,
critical
considerations
include
the
following:

• Performance
  speeds
• Power
  consumption
• Cost-efficiency
• Programmability
• Bandwidth

Understanding
graphics
processing
units
(GPUs)

GPUs
are
a
type
of
specialized
circuit
that
is
designed
to
rapidly
manipulate
memory
to
accelerate
the
creation
of
images.
Built
for
high
throughput,
they
are
especially
effective
for
parallel
processing
tasks,
such
as
training
large-scale
deep
learning
applications.
Although
typically
used
in
demanding
applications
like
gaming
and
video
processing,
high-speed
performance
capabilities
make
GPUs
an
excellent
choice
for
intensive
computations,
such
as
processing
large
datasets,
complex
algorithms
and
cryptocurrency
mining. 

In
the
field
of
artificial
intelligence,
GPUs
are
chosen
for
their
ability
to
perform
the
thousands
of
simultaneous
operations
necessary
for
neural
network
training
and
inference. 

Key
features
of
GPUs

• 
  High-performance:
  Powerful
  GPUs
  are
  adept
  at
  handling
  demanding
  computing
  tasks
  like
  
  high
  performance
  computing
  (HPC)
  and
  deep
  learning
  applications. 
• 
  Parallel
  processing:
  GPUs
  excel
  at
  tasks
  that
  can
  be
  broken
  down
  into
  smaller
  operations
  and
  processed
  concurrently. 

While
GPUs
offer
exceptional
computing
power,
their
impressive
processing
capability
comes
at
the
cost
of
energy
efficiency
and
high-power
consumption.
For
specific
tasks
like
image
processing,
signal
processing
or
other
AI
applications,

cloud-based
GPU
vendors
may
provide
a
more
cost-effective
solution
through
subscription
or
pay-as-you-go
pricing
models. 

GPU
advantages

• 
  High
  computational
  power:
  GPUs
  provide
  the
  high-end
  processing
  power
  necessary
  for
  the
  complex
  floating-point
  calculations
  that
  are
  required
  when
  training
  deep
  learning
  models. 
• 
  High
  speed:
  GPUs
  make
  use
  of
  multiple
  internal
  cores
  to
  speed
  up
  parallel
  operations
  and
  enable
  the
  efficient
  processing
  of
  multiple
  concurrent
  operations.
  GPUs
  can
  rapidly
  process
  large
  datasets
  and
  greatly
  decrease
  time
  spent
  training
  machine
  learning
  models.
• 
  Ecosystem
  
  support:
  GPU’s
  benefit
  from
  support
  by
  major
  manufacturers
  like
  Xilinx
  and
  Intel,
  with
  robust
  developer
  ecosystems
  and
  frameworks
  including
  CUDA
  and
  OpenCL.

GPU
challenges

• 
  Power
  consumption:
  GPUs
  require
  significant
  amounts
  of
  power
  to
  operate,
  which
  can
  increase
  operational
  expenses
  and
  also
  impact
  environmental
  concerns.
• 
  Less
  flexible:
  GPUs
  are
  far
  less
  flexible
  than
  FPGAs,
  with
  less
  opportunity
  for
  optimizations
  or
  customization
  for
  specific
  tasks. 

For
a
deeper
look
into
GPUs,
check
out
the
following
video:

Understanding
field
programmable
gate
arrays
(FPGAs)

FPGAs
are
programmable
silicon
chips
that
can
be
configured
(and
reconfigured)
to
suit
multiple
applications.
Unlike
application-specific
integrated
circuits
(ASICs),
which
are
designed
for
specific
purposes,
FPGAs
are
known
for
their
efficient
flexibility,
particularly
in
custom,
low-latency
applications.
In
deep
learning
use
cases,
FPGAs
are
valued
for
their
versatility,
power
efficiency
and
adaptability. 

While
general-purpose
GPUs
cannot
be
reprogrammed,
the
FPGA’s
reconfigurability
allows
for
specific
application
optimization,
leading
to
reduced

latency
and
power
consumption.
This
key
difference
makes
FPGAs
particularly
useful
for
real-time
processing
in
AI
applications
and
prototyping
new
projects. 

Key
features
of
FPGAs

• 
  Programmable
  hardware:
  FPGAs
  can
  be
  easily
  configured
  with
  FPGA-based
  hardware
  description
  languages
  (HDL),
  such
  as
  Verilog
  or
  VHDL.
• 
  Power
  Efficiency:
  FPGAs
  use
  less
  power
  compared
  to
  other
  processors,
  reducing
  operational
  costs
  and
  environmental
  impact. 

While
FPGAs
may
not
be
as
mighty
as
other
processors,
they
are
typically
more
efficient.
For
deep
learning
applications,
such
as
processing
large
datasets,
GPUs
are
favored.
However,
the
FPGA’s
reconfigurable
cores
allow
for
custom
optimizations
that
may
be
better
suited
for
specific
applications
and
workloads.

FPGA
advantages

• 
  Customization:
  Central
  to
  FPGA
  design,
  programmability
  supports fine-tuning
  and
  prototyping,
  useful
  in
  the
  emerging
  field
  of
  deep
  learning. 
• 
  Low
  latency:
  The
  reprogrammable
  nature
  of
  FPGAs
  makes
  them
  easier
  to
  optimize
  for
  real-time
  applications. 

FPGA
challenges

• 
  Low
  power:
  While
  FPGAs
  are
  valued
  for
  their
  energy
  efficiency,
  their
  low
  power
  makes
  them
  less
  suitable
  for
  more
  demanding
  tasks. 
• 
  Labor
  intensive:
  While
  programmability
  is
  the
  FPGA
  chip’s
  main
  selling
  point,
  FPGAs
  don’t
  just
  offer
  programmability,
  they
  require
  it.
  FPGA
  programming
  and
  reprogramming
  can
  potentially
  delay
  deployments. 

FPGA
vs.
GPU
for
deep
learning
use
cases

Deep
learning
applications,
by
definition,
involve
the
creation
of
a
deep
neural
network
(DNN),
a
type
of
neural
network
with
at
least
three
(but
likely
many
more)
layers.
Neural
networks
make
decisions
through
processes
that
mimic
the
way
biological
neurons
work
together
to
identify
phenomena,
weigh
options
and
arrive
at
conclusions.

Before
a
DNN
can
learn
to
identify
phenomena,
recognize
patterns,
evaluate
possibilities
and
make
predictions
and
decisions,
they
must
be
trained
on
large
amounts
of
data.
And
processing
this
data
takes
a
large
amount
of
computing
power.
FPGAs
and
GPUs
can
provide
this
power,
but
each
has
their
strengths
and
weaknesses.

FPGAs
are
best
used
for
custom,
low-latency
applications
that
require
customization
for
specific
deep
learning
tasks,
such
as
bespoke
AI
applications.
FPGAs
are
also
well
suited
for
tasks
that
value
energy
efficiency
over
processing
speeds.

Higher-powered
GPUs,
on
the
other
hand,
are
generally
preferred
for
heavier
tasks
like
training
and
running
large,
complex
models.
The
GPUs
superior
processing
power
makes
it
better
suited
for
effectively
managing
larger
datasets.    

FPGA
use
cases

Benefitting
from
versatile
programmability,
power
efficiency
and
low
latency,
FPGAs
are
often
used
for
the
following:  

• 
  Real-time
  
  processing:
  Applications
  requiring
  low-latency,
  real-time
  signal
  processing,
  such
  as
  digital
  signal
  processing,
  radar
  systems,
  autonomous
  vehicles
  and
  telecommunications.
• 
  Edge
  computing:
  
  Edge
  computing
  and
  the
  practice
  of
  moving
  compute
  and
  storage
  capabilities
  closer
  locally
  to
  the
  end-user
  benefit
  from
  the
  FPGA’s
  low
  power
  consumption
  and
  compact
  size.
• 
  Customized
  hardware
  acceleration:
  Configurable
  FPGAs
  can
  be
  fine-tuned
  to
  accelerate
  specific
  deep
  learning
  tasks
  and
  HPC
  clusters
  by
  optimizing
  for
  specific
  types
  of
  data
  types
  or
  algorithms. 

GPU
use
cases

General
purpose
GPUs
typically
offer
higher
computational
power
and
preprogrammed
functionality,
making
them
bust-suited
for
the
following
applications: 

• 
  High-performance
  computing:
  GPUs
  are
  an
  integral
  element
  of
  operations
  like
  
  data
  centers
  or
  research
  facilities
  that
  rely
  on
  massive
  computational
  power
  to
  run
  simulations,
  perform
  complex
  calculations
  or
  manage
  large
  datasets. 
• 
  Large-scale
  models:
  Designed
  for
  speedy
  parallel
  processing,
  GPUs
  are
  especially
  capable
  at
  calculating
  a
  large
  number
  of
  matrix
  multiplications
  concurrently
  and
  are
  often
  used
  to
  expedite
  training
  times
  for
  large-scale
  deep
  learning
  models.

Take
the
next
step

When
comparing
FPGAs
and
GPUs,
consider
the
power
of
cloud
infrastructure
for
your
deep
learning
projects.
With
IBM
GPU
on
cloud,
you
can
provision
NVIDIA
GPUs
for
generative
AI,
traditional
AI,
HPC
and
visualization
use
cases
on
the
trusted,
secure
and
cost-effective
IBM
Cloud
infrastructure.
Accelerate
your
AI
and
HPC
journey
with
IBM’s
scalable
enterprise
cloud.

Explore
GPUs
on
IBM
Cloud