Renesas Electronics Corporation (TSE: 6723), a premier supplier of
advanced semiconductor solutions, today announced it has developed an AI
accelerator that performs CNN (convolutional neural network) processing
at high speeds and low power to move towards the next generation of
Renesas embedded AI (e-AI), which will accelerate increased intelligence
of endpoint devices. A Renesas test chip featuring this accelerator has
achieved the power efficiency of 8.8 TOPS/W (Note 1), which is the
industry’s highest class of power efficiency. The Renesas accelerator is
based on the processing-in-memory (PIM) architecture, an increasingly
popular approach for AI technology, in which multiply-and-accumulate
operations are performed in the memory circuit as data is read out from
that memory.
To create the new AI accelerator, Renesas developed the following three
technologies. The first is a ternary-valued (-1, 0, 1) SRAM structure
PIM technology that can perform large-scale CNN computations. The second
is an SRAM circuit to be applied with comparators that can read out
memory data at low power. The third is a technology that prevents
calculation errors due to process variations in the manufacturing.
Together, these technologies achieve both a reduction in the memory
access time in deep learning processing and a reduction in the power
required for the multiply-and-accumulate operations. Thus, the new
accelerator achieves the industry’s highest class of power efficiency
while maintaining an accuracy ratio more than 99 percent when evaluated
in a handwritten character recognition test (MNIST).
Renesas presented these results on June 13, at the 2019 Symposia
on VLSI Technology and Circuits in Kyoto, Japan, June 9-14, 2019.
Renesas also demonstrated real-time image recognition using a prototype
AI module in which this test chip, powered by a small battery, was
connected with a microcontroller, a camera, other peripheral devices,
and development tools at the demonstration session.
Until now, the PIM architecture was unable to achieve an adequate
accuracy level for large-scale CNN computations with single-bit
calculations since the binary (0,1) SRAM structure was only able to
handle data with values 0 or 1. Furthermore, process variations in the
manufacturing resulted in a reduction in the reliability of these
calculations, and workarounds were required. Renesas has now developed
technologies that resolve these issues and will be applying these, as a
leading-edge technology that can implement revolutionary AI chips of the
future, to the next generation of e-AI solutions for applications such
as wearable equipment and robots that require both performance and power
efficiency.
Key Features of the Newly Developed Technology for Next-Generation AI
Chips:
Ternary (-1, 0, 1) SRAM structure PIM that can adjust its calculation
bit number according to the accuracy required
The ternary (-1, 0, 1) SRAM structure PIM architecture adopts a
combination of a ternary memory with a simple digital calculation block
to hold increases in the amount of hardware and increases in calculation
errors to a minimum. At the same time, it allows switching the number of
bits between, for example, 1.5-bit (ternary) and 4-bit calculations
according to the required accuracy. Since this can support different
required accuracies and calculation scales on a per-user basis, users
can optimize the balance between accuracy and power consumption.
High-precision/low-power memory data readout circuit that combines
comparators and replica cells
When a PIM architecture is adopted, memory data is read out by detecting
the value of the bit line current in the SRAM structure. Although it is
effective to use A/D converters for high-precision bit line current
detection, this approach has the issue of high-power consumption and
increased chip areas. In this effort, Renesas combined a comparator
(1-bit sense amplifier) with a replica cell in which the current can be
controlled flexibly to develop a high-precision memory data readout
circuit. Furthermore, these technologies take advantage of the fact that
the number of nodes (neurons) activated by neural network operation is
very small, about 1%, and achieves even lower power operation by
stopping operation of the readout circuits for nodes (neurons) that are
not activated.
Variation avoidance technology that suppresses calculation errors due
to process variations in the manufacturing
The PIM architecture has the challenge of calculation errors due to
process variations in the manufacturing. This is because process
variations in the manufacturing cause errors in the values of the bit
line currents in the SRAM structure, and as a result, errors occur in
the memory data readout. To resolve this issue, Renesas covered the
inside of the chip with multiple SRAM calculation circuit blocks and
used blocks with minimal manufacturing process variations to perform the
calculations. Since the activated nodes are only a small minority of all
nodes, activated nodes are allocated selectively to SRAM calculation
circuit blocks that have minimal manufacturing process variations to
perform the calculations. This allows calculation errors to be reduced
to a level where they can be essentially ignored.
Since introducing the embedded AI (e-AI) concept in 2015, Renesas has
moved forward with the development of several e-AI
solutions. Renesas has defined “classes” based on the effectiveness
of e-AI and applications that are implemented and has been developing
e-AI solutions based on the following four classes:
-
Class 1: Judging the correctness or abnormality of signal waveform
data. -
Class 2 (100 GOPS/W class): Judging correctness or abnormality using
real-time image processing. - Class 3 (1 TOPS/W class): Performing recognition in real time.
-
Class 4 (10 TOPS/W class): Enabling incremental learning at an
endpoint.
Renesas introduced an e-AI
development environment in 2017, and in 2018 announced the RZ/A2M
microprocessor, which integrates Renesas’ exclusive DRP (dynamically
reconfigurable processor) on chip. Renesas provides these technologies
for applications classified through class 2. To implement class 3
applications, Renesas has further improved the computational performance
of this DRP
technology.
Now, Renesas is unveiling the new, leading-edge technology developed
through this effort. The new accelerator technology combines both low
power consumption and improved computational performance and could be
one of the key technologies to implement future class 4 applications.
Renesas is committed to contributing to the realization of a smart
society through increased intelligence that applies AI at both edges and
endpoints in the IoT.
(Note 1) TOPS/W: Tera Operations per Second per Watt. This is a rating
that indicates the number of computations that can be performed in one
second with one watt of power. The performance of 8.8 TOPS/W corresponds
to performing 8.8 × 10^12 calculations in one second with one watt of
power.
About Renesas Electronics Corporation
Renesas Electronics Corporation (TSE:
6723) delivers trusted embedded design innovation with complete
semiconductor solutions that enable billions of connected, intelligent
devices to enhance the way people work and live. A global
leader in microcontrollers, analog, power, and SoC products, Renesas
provides comprehensive solutions for a broad range of automotive,
industrial, home electronics, office automation, and information
communication technology applications that help shape a limitless
future. Learn more at renesas.com.
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