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Every foundry and every node is different, but for every foundry/node there are multiple supported metal stacks.

Some chips use a lot more metal layers than others. A common rule of thumb is each metal layer increases wafer cost 10%. So, a chip with 5 more metal layers than another will cost 50%+ more.

One of the most critical ramifications of the emergence of quantum computers is the impact on security because quantum computers have the potential to break even the most secure encryption methods used today. That is why the industry will be seeing a rapid shift from traditional cryptosystems to Post Quantum Cryptography (PQC) systems in the next few years. PQC systems respond to this growing quantum threat because they are based on mathematical problems that cannot be solved efficiently with Shor’s algorithm, or by any other known quantum computing algorithm.

Flex Logix brings reconfigurability to customers designing for 5G, SmartNICs, computational storage, networking, data centers, base stations, AI, and machine learning Flex Logix has completed porting and delivered the EFLX 4K eFPGA IP core, both the Logic and DSP versions, on TSMC 7nm technology to its lead customer for integration into a production ASIC…

New strategic approach will accelerate market adoption of Flex Logix reconfigurable computing architecture, across multiple platforms Flex Logix Technologies, Inc. announced that it has opened access to its edge inference AI solutions. Available in early 2023, device manufacturers and systems companies who design chips can now license Flex Logix’s InferX AI technology, enabling broader access to the company’s eFPGA and edge inference IP solutions.

5G, networking, cloud storage, defense, smart home, automotive, and others – are looking to embedded FPGAs (eFPGA) to save power and reduce cost. All these applications demand reconfigurability with lower power/cost, but they also require strong security. Listen to this webinar recording on SemiWiki anytime!

Now portable applications can leverage the heterogeneous computing done at the data centers!

Longer chip lifetimes mean they need to adapt to security threats.

System companies are taking a more proactive role in co-designing their hardware and software roadmaps, so it’s no surprise that they are also driving the adoption of embedded FPGAs (eFPGA.) But why and why has it taken so long?

Dan is joined by Geoff Tate, CEO and Co-founder of Flex Logix. Geoff explains the embedded FPGA market, including some history, applications and challenges to deliver a product that customers really want. He provides some very relevant background on why Flex Logix has been so successful in this market, and what lies ahead.

The EE Times Silicon 100 list is out and there is a detailed write-up on Flex Logix on both the eFPGA and AI technologies. We also made their “Editor’s Eleven” list which is a smaller selection of companies that are making the news in EE Times, will continue making the news and are also the companies they think are setting the trends for the future.

The main concern is keeping the smart city systems and their data and functions safe, especially if the system is touching critical infrastructure. This article explains several ways including the flexible low power way by adding eFPGA to the systems. eFPGAs are increasing security and lowering power and cost.

Flex Logix Technologies announced that it has partnered with Intrinsic ID to ensure that any device using its eFPGA remains secure and can’t be modified maliciously, whether through physical attacks or remote hacking.

Flex Logix(R) Technologies, Inc., the leading supplier of embedded FPGA (eFPGA) IP, architecture and software, announced today that it has been selected to be part of a team of microelectronic industry leaders, led by Microsoft, to build a chip development platform with the utmost regard for security as demonstrated by the DoD RAMP Project. Flex Logix was chosen for its leading embedded (eFPGA) technology that enables chips to be reconfigurable after tape-out, allowing companies to adapt to new requirements, changing standards and protocols as needed.

Many systems use FPGAs because they are more efficient than processors for parallel processing. The area, power, and cost of FPGAs are driving system architects to look for a better solution. The solution is integrating FPGAs into the main SoC. Why? Because it saves power and cost by as much as 10X.

Flex Logix® Technologies, Inc. and CEVA, Inc. have announced today the world’s first successful silicon implementation using Flex Logix’s EFLX® embedded FPGA (eFPGA) connected to a CEVA-X2 DSP instruction extension interface.

Silicon Catalyst, the world’s only incubator focused exclusively on accelerating semiconductor solutions, is pleased to announce that Flex Logix® has joined as the newest member of its In-Kind Partner program (IKP). Portfolio companies in the Silicon Catalyst Incubator will have access to Flex Logix’s innovative embedded FPGA (eFPGA) IP and software, enabling silicon reconfigurability for use in their chip designs.

Before Covid-induced supply chain issues affected semiconductor availability and lead times, concerns about counterfeit parts and trusted supply chains were becoming the subject of many articles and discussions…

Flex Logix® Technologies, Inc., the leading supplier of embedded FPGA (eFPGA) IP, architecture and software, announced today that it has reached a significant milestone of signing licenses to develop more than 32 ASICs/SoCs integrating EFLX, with nearly half already working in silicon.

TO INCLUDE ANY FLEX LOGIX TECHNOLOGY FOR RESEARCH AND CHIP PROTOTYPING IN ALL AVAILABLE PROCESSES INCLUDING RADHARD Enables any US Government-funded research programs and activities to use reconfigurable computing IP for no license fees

FPGA has become strategic technology. It is strategically important to two very big, high-growth applications: Cloud data centers and Communications systems including 5G, and acquisitions of FPGA companies confirm this. Why? Because of Parallel programming, but FPGAs have some concerns. This presentation will talk about how embedding FPGAs (eFPGA) can change the use case for FPGA and the way software is controlled.

Machine vision is rapidly becoming a key enabling technology for digitalization and automation in automotive, healthcare, manufacturing, retail, smart buildings, smart cities, transportation, and logistics. According to ABI Research, a global technology intelligence firm, the total revenue of machine vision technology in the seven major markets is expected to reach US$36 billion by 2027, up from US$21.4 billion in 2022. This growth translates to a CAGR of 11%.

The X1 was specified to be a lean, high-performance edge accelerator for AI inference processing incorporating Flex Logix’s proprietary tensor processor, PCIe, DDR, memory, and a NoC. And we ran into an issue…

eFPGA Market has come a long ways over the years. Find out where its going in 2022.

Embedded FPGA (eFPGA) is the next big market for semiconductor IP. It can be used on almost every kind of digital chip and has a significant software value add as well—much like the market for embedded processors. When it comes to chip design, eFPGA provides competitive advantages that can add up to millions of dollars in savings and flexibility that wasn’t possible until now.

Consider these 6 factors when selecting an AI accelerator for your medical device.

It’s an exciting time to be a part of the rapidly growing AI industry, particularly in the field of inference. Once relegated simply to high-end and outrageously expensive computing systems, AI inference has been marching towards the edge at super-fast speeds. Today, customers in a wide range of industries – from medical, industrial, robotics, security, retail and imaging – are either evaluating or actually designing AI inference capabilities into their products and applications.

Why it’s so important to match the AI task to the right type of chip. Machine learning (ML)-based approaches to system development employ a fundamentally different style of programming than historically used in computer science. This approach uses example data to train a model to enable the machine to learn how to perform a task. ML training is highly iterative with each new piece of training data generating trillions of operations.