New Product Innovation Award 2019 - Frost Multimedia Wire · 2019-12-18 · development platforms. Another advantage of accelerator chips is their ease of integration into the host

FROST & SULLIVAN BEST PRACTICES AWARD

New Product Innovation Award 2019AI Accelerator Chips - NORTH AMERICA

BEST PRACTICES RESEARCH

© Frost & Sullivan 2019 2 “We Accelerate Growth”

Contents

Background and Company Performance ........................................................................ 3

Industry Challenges .............................................................................................. 3

New Product Attributes and Customer Impact .......................................................... 3

Conclusion........................................................................................................... 4

Significance of New Product Innovation ......................................................................... 8

Understanding New Product Innovation ......................................................................... 8

Key Benchmarking Criteria .................................................................................... 9

Best Practices Award Analysis for Gyrfalcon Technology Inc ............................................. 9

Decision Support Scorecard ................................................................................... 9

New Product Attributes ....................................................................................... 10

Customer Impact ............................................................................................... 10

Decision Support Matrix ...................................................................................... 11

Best Practices Recognition: 10 Steps to Researching, Identifying, and Recognizing Best Practices ................................................................................................................. 12

The Intersection between 360-Degree Research and Best Practices Awards ..................... 13

Research Methodology ........................................................................................ 13

About Frost & Sullivan .............................................................................................. 13



Background and Company Performance

Industry Challenges

The IoT (internet of things) industry has undergone radical changes in recent years, with

the constant evolution of groundbreaking technologies. In particular, artificial intelligence

(AI) and Big Data have been key enablers for the rapid development of the connected and

smart devices industry as they bring in a new dimension to devices by making them

intelligent. Frost & Sullivan research shows the technology industry is keen on integrating

AI into electronic devices to leverage benefits, such as sharpening machine hearing,

speech, and vision; accessing the growing volumes of stored data; and enabling devices to

learn from experience.

The research on AI is a global phenomenon that is being ardently pursued by global

universities and research institutes, such as Stanford and Cambridge. AI’s initial

developments centered around academic research include a few theories on AI

implementation cases that were demonstrated using mainframe systems and graphic

processing units (GPUs); however, this AI research was not feasible to be carried out on a

larger scale or transpired into a business venture. The introduction of convolutional neural

networks (CNN) revolutionized the AI industry; however, the AI ecosystem had yet to

evolve. Chipsets consumed more power and time for processing AI algorithms and

occupied significant space in the circuits, making integration into electronic devices, such

as smartphones, difficult.

Another important criterion to consider is the cost involved in commercializing chips. To

achieve high adoption, developed chips must cater to millions of users or IOT endpoints,

where they are deployed in bulk quantities. Furthermore, the AI industry is coming from

the orientation of GPUs and mainframes and relies heavily on AI experts or PhDs to create

algorithms, thereby stalling AI’s evolution because advanced AI hardware and software

platforms typically require specialized training for professionals to optimize their use in

specific applications.

Founded in 2017 by a team of AI, memory, and semiconductor veterans, California-based

Gyrfalcon Technology Inc (GTI) has created a domain-specific architecture that can be

optimized for AI applications and integrated into mature process technologies to reduce

the cost of producing AI chips, while attaining industry-level performance with reduced

energy consumption. In 2016, the company’s founders’ initial proof of concept used an

off-the-shelf field-programmable gate array (FPGA). GTI has a global presence, with

offices in China, Japan, and Korea for catering to the AI needs of customers worldwide.

Within a year of inception, GTI successfully released the first of its Lightspeeur© AI

Accelerator chip, followed by two more in 2018. These chips can be integrated into a

variety of smart devices and cloud platforms, thereby paving the way for the next

generation of AI devices.



New Product Attributes and Customer Impact

Design and Quality

After the initial success achieved by demonstrating the proof of concept on a FPGA

system, GTI focused on designing chipsets for optimizing AI. With the vision to make AI

efficiently deliver high performance with low energy consumption, the company invested

in creating proprietary technologies to enable the development AI accelerator chips that

can be integrated into smart devices. To address the design constraints evident in the AI

industry, GTI incorporated the unique approach of using their proprietary Matrix

Processing Engine (MPE) for processing two-dimensional (2D) data, which mainly

comprises images or voice data, and utilizing its proprietary Constreaming™ Engine

technology. As a result, the input 2D data is maintained in its original form because it is

rapidly processed through the matrix architecture without wasting any energy to convert

it, unlike time- and energy-consuming GPUs and other AI chip methods that use vector

technology for data conversion.

Another element of the MPE is GTI’s proprietary AI

processing in memory (APiM) that integrates memory

and logic into a single chip. This technology improves

performance and reduces energy consumption by

avoiding any unnecessary data movement between the

processor and discrete memory units. This

breakthrough chip 2801 is the first accelerator chip in

GTI’s Lightspeeur portfolio and performs with 9.3 tera

operations per seconds per watt (TOPs/watt) with a

peak performance of 5.6 TOPs at 100 MHz, which can

also consume power as low as 300 milliwatts (mW).

The size of this ASIC chip is 7 mm x 7 mm using 28 nm process technology.

The second chip launched was the 2803, which was optimized for advanced edge and data

center use cases, such as Cloud AI operations. It delivers 24 TOPs/watt and consumes a

minimum power of 700 mW. It can be offered in a multiple chip configuration (16 per PCIe

card) to deliver 270 TOPS while using only 28 watts of power. Both chips are cost

effective, energy efficient, compact, and compare favorably against other AI chip solutions

which require a more circuit area and consume much more power, while delivering lower

performance in terms of TOPs. Therefore, GTI’s chips are ideal for running AI applications,

such as audio and video processing.

The third chip launched, the 2802 is yet another innovation from GTI, in collaboration with

its semiconductor fabrication partner Taiwan Semiconductor Manufacturing Company

Limited (TSMC), released in December 2018. GTI developed its proprietary Gyrfalcon

MRAM engine (GME) to be the first AI chip in the industry with integrated memory

magnetic random access memory (MRAM). As more AI accelerator solutions are trying to

pair memory and logic, this is a key advantage to GTI’s set of technologies and designs.

Lightspeeur© AI

Accelerator Chip 2801 Image Source: GTI



With the built-in non-volatile memory of 40 megabytes (MB), this chip can run large AI

models, or the memory can be partitioned to run concurrent models on a single chip.

Using non-volatile memory enables the deployment of applications in remote areas and

can be powered by alternative energy generation technologies, such as solar. In addition,

the memory does not need any lead time to load the models or any other information

pertaining to the AI application, which is a great advantage for IoT endpoints.

GTI has a strong portfolio of IP, with more than 50 patents, awarded or in process,

protecting its technology against infringement.

GTI’s AI accelerator chips can be integrated into host processes, thereby boosting the

performance of AI applications. For example, AI data coming from various device sensors

invariably goes to the host processor that runs the application. The data is then delivered

to the AI accelerator for further processing based on the model embedded on the chip,

and the calculated result is returned to the application running on the host device. Based

on application requirements, models can be designed and embedded in the AI accelerator

chip and can be replaced based on the application running on the host device.

GTI uses the 22 nm processing technology, which can be implemented in many

semiconductor fabrication facilities to create chipset

modules with high performance and low energy chips

deployed for an edge-AI product.

Frost & Sullivan commends GTI’s ability to develop a

cutting-edge technology for creating AI accelerator chips

that can be integrated into a variety of host processors

embedded in electronic devices, such as smartphones,

tablets, and industrial robots.

Reliability

Software algorithms are improving every year, and GTI addresses this rapid change by

providing its AI chips with a flexible architecture so that it can be deployed to the neural

net topology of Caffe, TensorFlow, and PyTorch, which are the three most widely used AI

development platforms. Another advantage of accelerator chips is their ease of integration

into the host processor of any company; therefore, these chips are ideally suited for cloud

applications, in addition to edge-AI applications. Moreover, by integrating multiple 2803

chips (16 typically) into the peripheral component interconnect express (PCIe) card, along

with the host processor, multiple AI applications can be run in parallel.

Compared to other AI accelerator chips in the market, GTI’s chips offer ten times more

efficiency in terms of performance to power usage, which can drastically reduce

datacenter costs. The PCIe card integrated with the 2803 chip delivers a performance of

270 TOPs and consumes only 28 watts, which is high compared to other AI-based PCIe

card vendors that offer either 65 TOPs at 70 watts or 248 TOPs at 300 watts. In addition,

the thermal footprint of GTI’s chips are more effective than other chips because they do

not generate as much heat, thereby eliminating any need for additional cooling

equipment. Cascading is another unique feature to the 2803 chip, allowing large AI

8 Chip PCIe Card

Image Source: GTI



models to be run in datacenters across a limitless number of 2803 chips that have been

embedded in the datacenter rack.

Based on Frost & Sullivan analysis, GTI’s AI accelerator chips exhibit high reliability in

extreme data-centric operations because performance is not compromised, and energy

consumption is kept at a minimum. Furthermore, the cascading architecture enables the

modeling of complex AI applications, which is not feasible with existing competing chips.

Customer Ownership Experience and Customer Service Experience

The launch of GTI’s 2801 chip created a new wave in the AI industry, which had been

bogged down by complex GPUs and mainframe systems. The 2801 chip garnered

immediate interest from Tier I companies, such as Samsung and LG, which have been

actively creating next-generation devices, with a heavy focus on AI. GTI’s AI accelerator

chips are analogous to the bricks of a building because they form key building blocks for

constructing any kind of AI application. The core functionality of these AI accelerator chips

is the processing of input AI data, irrespective of its format (e.g., visual or audio), using

GTI’s patented technologies MPE and APiM and running the data through the chip

embedded with a model optimized for a particular application. The chips can boost any

application, such as AI thermostats, sensor hubs, drones, and robots. Regardless of

application, the core technology executes in the same way, thus providing customers with

a sense of ease in deploying the chips, without undergoing any design changes. In

addition, GTI supports its clients in modifying the accelerator’s operating parameters, such

as higher TOPS for one chip running bigger models.

One of the key challenges in the AI industry is the lack of awareness in the developer

community because of the complexity involved in creating an AI ecosystem. With the goal

of making its products more accessible, GTI offers development kits to customers to help

them create simple AI models and provide a first-hand experience. The proprietary

universal serial bus (USB) stick called Plai™ Plug embedded with a 2801 chip, along with

the Plai™Builder software, provides customers with the ability to create independent

model designs and facilitate the development of a proof of concept. The ability to provide

requisite tools for creating independent model designs is in line with GTI’s goal of enabling

customers to be independent and masters of their own models, while continuing to

provide the building blocks in the form of AI chips. Moreover, GTI supports

customers in designing custom chips if they want dedicated chips with specific

parameters pertaining to their applications.

Apart from Samsung and LG, GTI’s clientele consists of several prominent industry

leaders, such as Foxconn, Alibaba Group, Socionext, ArcSoft, and Konica Minolta. In

collaboration with GTI, Socionext, a global system-on-chip (SoC) developer, has created

an AI server solution by integrating its proprietary parallel multi-core processor

SynQuacer™ SC2A11 with GTI’s AI accelerator chips, which are leveraged for developing a

video and image analysis system for surveillance, live streaming, and other video

applications. QuickLogic is considering the possibility of leveraging GTI’s AI chips in its



next-generation sensor hubs used for recognizing wake words in voice recognition-capable

gadgets. LG is expected to release its AI products embedded with GTI’s chips in 2019.

GTI’s value proposition is to optimize the integration of AI into chip designs to deliver the

best ratio of high performance-to-low energy consumption with reduced production costs.

Compared to competing solutions, GTI’s technology offers more design flexibility because

the customer can mix and match whatever chips they want based on the end application.

For example, when building a robot, one 2801 chip can be used to make a simple robot, or

several 2801 chips can be packaged into a card and used to enhance capabilities by

running multiple functions in parallel. Moreover, GTI’s accelerator chips aggregate the

processing power and allow customers to conduct advanced edge and cloud data

computing operations/implementations.

Frost & Sullivan recognizes GTI’s successful ability to address the emerging demand for AI

applications through its series of AI accelerator chips and its ability to enable customers to

develop AI applications cost effectively based on their requirements.

Conclusion

Advancements in algorithms and software solution tools, such as deep learning and

pattern recognition, are driving the demand for faster processing chips and systems that

need to be integrated into most electronic devices that use AI. GTI designed its AI

accelerator chip for the seamless integration into electronic devices, improved energy

efficiency, lower production costs, and improved ease of use.

GTI’s success establishes a benchmark for companies planning to venture into the AI chip

market, which will be a major contributor to the growth of smart devices in the near

future. GTI expects to stay ahead in the competitive AI accelerator chip industry through

its innovations and customer-centric upgradations, thus earning the trust and accolades

from its clients.

For its strong overall performance, Gyrfalcon Technology Inc., has earned Frost &

Sullivan’s 2019 New Product Innovation Award in the North American AI accelerator chips

industry.



Significance of New Product Innovation

Ultimately, growth in any organization depends on continually introducing new products to

the market and successfully commercializing those products. For these dual goals to

occur, a company must be best in class in three key areas: understanding demand,

nurturing the brand, and differentiating from the competition.

Understanding New Product Innovation

Innovation is about finding a productive outlet for creativity—for consistently translating

ideas into high-quality products that have a profound impact on the customer.



Key Benchmarking Criteria

For the New Product Innovation Award, Frost & Sullivan analysts independently evaluated

2 key factors—New Product Attributes and Customer Impact—according to the criteria

identified below.

New Product Attributes

Criterion 1: Match to Needs

Criterion 2: Reliability

Criterion 3: Quality

Criterion 4: Positioning

Criterion 5: Design

Customer Impact

Criterion 1: Price/Performance Value

Criterion 2: Customer Purchase Experience

Criterion 3: Customer Ownership Experience

Criterion 4: Customer Service Experience

Criterion 5: Brand Equity

Best Practices Award Analysis for Gyrfalcon Technology

Decision Support Scorecard

To support its evaluation of best practices across multiple business performance categories, Frost & Sullivan employs a customized Decision Support Scorecard. This tool allows research and consulting teams to objectively analyze performance according to the key benchmarking criteria listed in the previous section, and to assign ratings on that

basis. The tool follows a 10-point scale that allows for nuances in performance evaluation. Ratings guidelines are illustrated below.

RATINGS GUIDELINES

The Decision Support Scorecard considers New Product Attributes and Customer Impact

(i.e., the overarching categories for all 10 benchmarking criteria; the definitions for each

criterion are provided beneath the scorecard). The research team confirms the veracity of

this weighted scorecard through sensitivity analysis, which confirms that small changes to

the ratings for a specific criterion do not lead to a significant change in the overall relative

rankings of the companies.



The results of this analysis are shown below. To remain unbiased and to protect the

interests of all organizations reviewed, Frost & Sullivan has chosen to refer to the other

key participants as Competitor 1 and Competitor 2.

Measurement of 1–10 (1 = poor; 10 = excellent)

New Product Innovation New Product Attributes

Customer Impact

Average

Rating

Gyrfalcon Technology Inc 9 9 9

Competitor 1 8 8 8

Competitor 2 7 7 7


Criterion 1: Match to Needs

Requirement: Customer needs directly influence and inspire the product’s design and

positioning.

Criterion 2: Reliability

Requirement: The product consistently meets or exceeds customer expectations for

consistent performance during its entire life cycle.

Criterion 3: Quality

Requirement: Product offers best-in-class quality, with a full complement of features and

functionalities.

Criterion 4: Positioning

Requirement: The product serves a unique, unmet need that competitors cannot easily

replicate.

Criterion 5: Design

Requirement: The product features an innovative design, enhancing both visual appeal

and ease of use.

Customer Impact

Criterion 1: Price/Performance Value

Requirement: Products or services offer the best value for the price, compared to similar

offerings in the market.

Criterion 2: Customer Purchase Experience

Requirement: Customers feel they are buying the optimal solution that addresses both

their unique needs and their unique constraints.

Criterion 3: Customer Ownership Experience

Requirement: Customers are proud to own the company’s product or service and have a

positive experience throughout the life of the product or service.



Criterion 4: Customer Service Experience

Requirement: Customer service is accessible, fast, stress-free, and of high quality.

Criterion 5: Brand Equity

Requirement: Customers have a positive view of the brand and exhibit high brand loyalty.

Decision Support Matrix

Once all companies have been evaluated according to the Decision Support Scorecard,

analysts then position the candidates on the matrix shown below, enabling them to

visualize which companies are truly breakthrough and which ones are not yet operating at

best-in-class levels.

High

Low

Low High

Cu

sto

mer I

mp

act


Gyrfalcon Technology Inc

Competitor 1

Competitor 2



Best Practices Recognition: 10 Steps to Researching, Identifying, and Recognizing Best Practices

Frost & Sullivan analysts follow a 10-step process to evaluate award candidates and

assess their fit with select best practices criteria. The reputation and integrity of the

awards are based on close adherence to this process.

STEP OBJECTIVE KEY ACTIVITIES OUTPUT

1 Monitor, target, and

screen

Identify award recipient candidates from around the

world

Conduct in-depth industry research

Identify emerging industries Scan multiple regions

Pipeline of candidates that potentially meet all best

practices criteria

2 Perform 360-degree

research

Perform comprehensive, 360-degree research on all

candidates in the pipeline

Interview thought leaders and industry practitioners

Assess candidates’ fit with best practices criteria

Rank all candidates

Matrix positioning of all candidates’ performance

relative to one another

3

Invite thought

leadership in best

practices

Perform in-depth examination of all candidates

Confirm best practices criteria

Examine eligibility of all candidates

Identify any information gaps

Detailed profiles of all ranked candidates

4

Initiate research

director review

Conduct an unbiased evaluation of all candidate

profiles

Brainstorm ranking options Invite multiple perspectives

on candidates’ performance Update candidate profiles

Final prioritization of all eligible candidates and

companion best practices positioning paper

5

Assemble

panel of industry

experts

Present findings to an expert

panel of industry thought leaders

Share findings

Strengthen cases for candidate eligibility

Prioritize candidates

Refined list of prioritized

award candidates

6

Conduct global

industry review

Build consensus on award candidates’ eligibility

Hold global team meeting to review all candidates

Pressure-test fit with criteria Confirm inclusion of all

eligible candidates

Final list of eligible award candidates, representing

success stories worldwide

7 Perform

quality check

Develop official award

consideration materials

Perform final performance

benchmarking activities Write nominations

Perform quality review

High-quality, accurate, and

creative presentation of nominees’ successes

8

Reconnect

with panel of industry

experts

Finalize the selection of the

best practices award recipient

Review analysis with panel

Build consensus Select recipient

Decision on which company

performs best against all best practices criteria

9 Communicate

recognition

Inform award recipient of recognition

Announce award to the CEO Inspire the organization for

continued success Celebrate the recipient’s

performance

Announcement of award and plan for how recipient

can use the award to enhance the brand

10 Take

strategic action

Upon licensing, company is

able to share award news with stakeholders and

customers

Coordinate media outreach

Design a marketing plan Assess award’s role in

strategic planning

Widespread awareness of

recipient’s award status among investors, media

personnel, and employees



The Intersection between 360-Degree Research and Best Practices Awards

Research Methodology

Frost & Sullivan’s 360-degree research

methodology represents the analytical

rigor of the research process. It offers a

360-degree view of industry challenges,

trends, and issues by integrating all 7 of

Frost & Sullivan’s research methodologies.

Too often companies make important

growth decisions based on a narrow

understanding of their environment,

resulting in errors of both omission and

commission. Successful growth strategies

are founded on a thorough understanding

of market, technical, economic, financial,

customer, best practices, and demographic

analyses. The integration of these research

disciplines into the 360-degree research

methodology provides an evaluation

platform for benchmarking industry

participants and for identifying those performing at best-in-class levels.

About Frost & Sullivan

Frost & Sullivan, the Growth Partnership Company, helps clients accelerate growth and

achieve best-in-class positions in growth, innovation, and leadership. The company's

Growth Partnership Service provides the CEO and the CEO's growth team with disciplined

research and best practices models to drive the generation, evaluation, and

implementation of powerful growth strategies. Frost & Sullivan leverages nearly 60 years

of experience in partnering with Global 1000 companies, emerging businesses, and the

investment community from 45 offices on 6 continents. To join Frost & Sullivan’s Growth

Partnership, visit http://www.frost.com.

360-DEGREE RESEARCH: SEEING ORDER IN THE CHAOS

http://www.frost.com/

Documents

New Product Innovation Award 2019 - Frost Multimedia Wire · 2019-12-18 · development platforms. Another advantage of accelerator chips is their ease of integration into the host