System On A Chip Buy
Buying an off-the-shelf chip timing system is a good option if you want to start lowering the costs of your race timing straight away without having to go very deep into understanding and picking individual RFID components.
system on a chip buy
Most chip timing systems (with some exceptions) come with some sort of coding which means they can only read RFID tags encoded by the same manufacturer. So instead of being able to go out to the open market and purchase RFID tags for a few cents, you are locked in to purchasing encoded RFID tags from the manufacturer only for a multiple of the open-market price.
Why do manufacturers do this? Well, the obvious answer is "money". Although a typical system will cost several thousands of dollars to purchase, the cost of disposable chips for the lifetime of the system will often exceed that - by a very long way. So it makes sense for the manufacturer to want to capture that recurring revenue.
Some chip timing systems are sold with software from the manufacturer. In other cases, the manufacturer will offer their own timing software at a price. Pretty much all systems will be compatible with a number of third-party timing software, and that might be your best option.
In terms of the economics of renting, you are basically looking at the cost of hiring a chip timing company without paying the costs for the crew that would come with it, which is a good saving since travel and accommodation is a big part of the race timer fee.
You can rent your chip timing system either direct from the manufacturer, through a regional distributor or sometimes even race timing companies (although the latter would probably try to steer you towards getting the full timing service from them).
Chip stocks are down, but they're far from out right now. In fact, global chip sales are expected to go on an epic run over the next decade, driven by myriad secular growth trends ranging from cloud computing to artificial intelligence (AI) to electric vehicles. Various estimates point to semiconductors going from about a $600 billion industry in 2022 to well over $1 trillion by the end of the decade.
It will be a bumpy ride up, though, since chip sales are cyclical. In fact, a down cycle is in effect right now. But two little-known, yet critical companies to the semiconductor industry -- Synopsys (SNPS 0.24%) and Cadence Design Systems (CDNS 0.49%) -- could be fantastic investments along the way. Here's why these two software plays on the computing hardware space are key stocks to consider for 2023.
To push computing performance to new heights, chip designers are adopting new ways of packaging semiconductor systems -- like chiplets, multiple smaller chips assembled into a larger integrated circuit. Some chip designers like Intel think multiple vendors will be involved in supplying the parts for a chiplet computing architecture model.
Other innovations include embedding software into chips early in the design process. Software technologies are also helping speed up development times with features like molecular-level simulation of chips, AI tools to solve design problems, and using pre-designed customizable circuits to accelerate overall design.
All of this requires a complex suite of software to coordinate all the various engineering teams and stakeholders involved in designing modern computing systems. That's where Synopsys and Cadence come in. There is a lot of overlap between what these two software companies do, but together -- along with Mentor, which is now owned by German industrial conglomerate Siemens -- they are a critical part of the chip industry known as electronic design automation (EDA) software. Be it a semiconductor designer, a chip manufacturer, a computing system assembler, or a software engineer, there's a good chance that Synopsys or Cadence is an important part of the software lineup being used.
And because Synopsys and Cadence are mostly subscription-based models, they put up consistent growth numbers. That stands in stark contrast to many other chip companies that ebb and flow with the economy or cycles of semiconductor sales booms and busts.
Tensor Processing Unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google for neural network machine learning, using Google's own TensorFlow software. Google began using TPUs internally in 2015, and in 2018 made them available for third party use, both as part of its cloud infrastructure and by offering a smaller version of the chip for sale.
The tensor processing unit was announced in May 2016 at Google I/O, when the company said that the TPU had already been used inside their data centers for over a year. The chip has been specifically designed for Google's TensorFlow framework, a symbolic math library which is used for machine learning applications such as neural networks. However, as of 2017 Google still used CPUs and GPUs for other types of machine learning. Other AI accelerator designs are appearing from other vendors also and are aimed at embedded and robotics markets.
Google's TPUs are proprietary. Some models are commercially available, and on February 12, 2018, The New York Times reported that Google "would allow other companies to buy access to those chips through its cloud-computing service." Google has said that they were used in the AlphaGo versus Lee Sedol series of man-machine Go games, as well as in the AlphaZero system, which produced Chess, Shogi and Go playing programs from the game rules alone and went on to beat the leading programs in those games. Google has also used TPUs for Google Street View text processing and was able to find all the text in the Street View database in less than five days. In Google Photos, an individual TPU can process over 100 million photos a day. It is also used in RankBrain which Google uses to provide search results.
The second-generation TPU was announced in May 2017. Google stated the first-generation TPU design was limited by memory bandwidth and using 16 GB of High Bandwidth Memory in the second-generation design increased bandwidth to 600 GB/s and performance to 45 teraFLOPS. The TPUs are then arranged into four-chip modules with a performance of 180 teraFLOPS. Then 64 of these modules are assembled into 256-chip pods with 11.5 petaFLOPS of performance. Notably, while the first-generation TPUs were limited to integers, the second-generation TPUs can also calculate in floating point. This makes the second-generation TPUs useful for both training and inference of machine learning models. Google has stated these second-generation TPUs will be available on the Google Compute Engine for use in TensorFlow applications.
The third-generation TPU was announced on May 8, 2018. Google announced that processors themselves are twice as powerful as the second-generation TPUs, and would be deployed in pods with four times as many chips as the preceding generation. This results in an 8-fold increase in performance per pod (with up to 1,024 chips per pod) compared to the second-generation TPU deployment.
In July 2018, Google announced the Edge TPU. The Edge TPU is Google's purpose-built ASIC chip designed to run machine learning (ML) models for edge computing, meaning it is much smaller and consumes far less power compared to the TPUs hosted in Google datacenters (also known as Cloud TPUs). In January 2019, Google made the Edge TPU available to developers with a line of products under the Coral brand. The Edge TPU is capable of 4 trillion operations per second with 2 W of electrical power.
On January 2, 2020, Google announced the Coral Accelerator Module and Coral Dev Board Mini, to be demonstrated at CES 2020 later the same month. The Coral Accelerator Module is a multi-chip module featuring the Edge TPU, PCIe and USB interfaces for easier integration. The Coral Dev Board Mini is a smaller SBC featuring the Coral Accelerator Module and MediaTek 8167s SoC.
Google followed the Pixel Neural Core by integrating an Edge TPU into a custom system-on-chip named Google Tensor, which was released in 2021 with the Pixel 6 line of smartphones. The Google Tensor SoC demonstrated "extremely large performance advantages over the competition" in machine learning-focused benchmarks; although instantaneous power consumption also was relatively high, the improved performance meant less energy was consumed due to shorter periods requiring peak performance.
A microcontroller unit (MCU) contains a CPU, memory, and input/output peripherals on a single integrated circuit (IC) chip and works as a standalone small computer. This allows for a reduction in power consumption, more compact designs, and cost savings. Additionally, microcontrollers can provide functional safety and security for embedded systems.
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