uiux
Regular
I can imagine heaps of stuff but I won't say it's real unless it is real
BRN Discussion Ongoing
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I can imagine heaps of stuff but I won't say it's real unless it is real
Sirod69
bavarian girl ;-)
Techinvestor17
Regular
I think Rob likes all sorts of stuff to make people aware of his/Brainchips presence. It’s an easy way to increase exposure. I would think that whoever controls the accounts may see that he has liked something and wonder more about him with the potential to drum up business.
uiux
Regular
sorry you are right,
I'll phrase it differently next time, ok
Lots of us have gone to the effort to research literally everything available:
Fundamental Analysis
HI all. I have been sitting back just watching as the chaos settles thinking of how to make this site a more friendly place for fundamental analysis and also a more easily readable and navigable resource. As everyone is aware I love contributing research that has been collated and themed...
thestockexchange.com.au
Generally we are completionists and take a scorched earth approach
It's a useful resource, don't be afraid to lean on it
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Diaper Odour Detector - WTF, not needed, the old human nose picks that shit up real quick (excuse the pun) and nappies are already attached to their own alarm systemAll the applications this Renesas ZMOD4410 sensor can be used for is endless.
Makeme 2020
Regular
alwaysgreen
Top 20
BrainChip and Weebit taking over the world is my dream scenario
Brainchip and WBT ?
CEA–Leti has made significant developments in pMUT sensors and spiking neural networks based on RRAM technology during the last decade. “We would like to thank the H2020 MeM–Scales project [871371] that partially funded the work,” Vianello said.
The present study demonstrates that combining visual sensors such as DVS cameras with the suggested pMUT–based hearing sensor should be investigated to create future consumer robots.
Neuromorphic Device with Low Power Consumption
By Maurizio Di Paolo Emilio 08.01.2022 0
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Compact, low–latency, and low–power computer systems are required for real–world sensory–processing applications. Hybrid memristive CMOS neuromorphic architectures, with their in–memory event–driven computing capabilities, present an appropriate hardware substrate for such tasks.
To demonstrate the full potential of such systems and drawing inspiration from the barn owl’s neuroanatomy, CEA–Leti has developed an event–driven, object–localization system that couples state–of–the–art piezoelectric, ultrasound transducer sensors with a neuromorphic computational map based on resistive random–access memory (RRAM).
CEA–Leti built and tested this object tracking system with the help of researchers from CEA–List, the University of Zurich, the University of Tours, and the University of Udine.
The researchers conducted measurements findings from a system built out of RRAM–based coincidence detectors, delay–line circuits, and a fully customized ultrasonic sensor. This experimental data has been used to calibrate the system–level models. These simulations have then been used to determine the object localization model’s angular resolution and energy efficiency. Presented in a paper published recently in Nature Communications, the research team describes the development of an auditory–processing system that increases energy efficiency by up to five orders of magnitude compared with conventional localization systems based on microcontrollers.
“Our proposed solution represents a first step in demonstrating the concept of a biologically inspired system to improve efficiency in computation,” said Elisa Vianello, senior scientist and edge AI program coordinator and senior author of the paper. “It paves the way toward more complex systems that perform even more sophisticated tasks to solve real–world problems by combining information extracted from different sensors. We envision that such an approach to conceive a bio–inspired system will be key to build the next generation of edge AI devices, in which information is processed locally and with minimal resources. In particular, we believe that small animals and insects are a great source of inspiration for an efficient combination of sensory information processing and computation. Thanks to the latest advancements in technology, we can couple innovative sensors with advanced RRAM–based computation to build ultra–low–power systems.”
BIO–INSPIRED ANALOG RRAM–BASED CIRCUIT
Two essential ideas underpin biological signal processing: event–driven sensing and in–memory analog processing.
“The goal is, as always, to get the best power efficiency for the level of performance needed by a specific application,” Vianello said. “Further improvements in energy efficiency are certainly possible with our system. For example, one could optimize our design and implement it in a more advanced technological node or with a specific low–power technology such as FD–SOI for the same level of performance. Concerning accuracy, our limiting factor is SNR. We have a clear performance/consumption tradeoff with the amplitude of the emitted pulse or the number of TX membranes, but technological advancement resulting in increased piezoelectric micromachined ultrasonic transducer [pMUT] sensitivity would also help improve the SNR for no extra power consumption. The use of pulses with good autocorrelation properties would be an interesting development in that sense if the matched filtering could be done with a small overhead.”
The team leveraged CEA–Leti’s successes in building pMUTs and its developments in RRAM–based spiking neural networks. The initial difficulty for the researchers was to create a pre–processing pipeline that pulls critical information from pMUTs, which encode information using brief events or spikes. This temporal encoding of the signal saves energy over standard continuous analog or digital data because only relevant data is handled.
PMUTs are becoming one of the most demanding ultrasonic systems due to their ability to create and detect ultrasound signals at the microscale in a highly efficient and well–controlled manner. The high–yield MEMS production technique, combined with thin–film piezoelectric materials (AlN, AlScN, PZT, etc.), enhances PMUT systems. Furthermore, the ability to install thin–film piezoelectric materials in a CMOS–compatible manner opens the door to innovative, extremely small systems that use the same substrate for the sensor and the conditioning electronics.
With this scenario, PMUT transducers are pushing the applicability of ultrasound as a physical magnitude in a variety of systems where size, power, sensitivity, and cost are important. These include intravascular medical imaging, biometric identification, gesture recognition, rangefinders, proximity sensors, acoustic wireless communication systems, acoustophoresis, photoacoustic systems, and so on.
Elisa Vianello
According to Vianello, pMUT devices are mature for industrialization. “One of the main restrictions to the development of pMUT devices is the competition of bulk PZT transducer and cMUT MEMs transducers. Bulk PZT transducers are easy to prototype and relatively cheap for low–volume production. cMUT MEMS transducers are more appropriate for biomedical applications due to their higher bandwidth and higher output pressure. One of the physical limitations of pMUT is the relatively low Q factor that results in transient regime that is detrimental to the spatial resolution and may impede short–distance measurements. Industrially matured piezoelectric materials for pMUT are PZT and AlN. PZT is more appropriate for actuating and AlN for sensing. For this application, we need both actuation and sensing, and our approach would have been valid with either of these materials. Yet we choose AlN because the four–electrode–pair scheme, which is not possible with PZT material, partially balances the relatively low output pressure per volt. Moreover, output pressure may be easily increased by the use of higher actuation voltage, at the price of higher consumption.”
Another difficulty was developing and building an analog circuit based on biologically inspired RRAM to analyze extracted events and estimate an object’s location. RRAM is a non–volatile technology that suits the asynchronous nature of events in the team’s proposed system, resulting in negligible power usage while the system is idle.
RRAM stores information in its non–volatile conductive state. The primary operational assumption of this technology is that altering the atomic state via precise programming operations controls the conductance of the device.
The researchers used an oxide–based RRAM with a 5–nm hafnium–dioxide layer sandwiched between top and bottom electrodes made of titanium and titanium nitride. By applying current/voltage waveforms that construct or break a conductive filament made up of oxygen vacancies between the electrodes, the conductivity of an RRAM device may be changed. They co–integrated these devices in a standard 130–nm CMOS process to build a reconfigurable neuromorphic circuit that included coincidence detectors and delay–line circuits (Figure 1). The non–volatile and analog nature of these devices perfectly match the event–driven nature of the neuromorphic circuits, resulting in low power consumption.
The circuit has an instant on/off feature: It begins operating immediately after being turned on, allowing the power supply to be entirely shut off as soon as the circuit is idle. Figure 1 displays the basic building block of the proposed circuit. It is composed of N parallel one–resistor–one–transistor (1T1R) structures that contain synaptic weights and is used to extract a weighted current that is then injected into a common differential pair integrator (DPI) synapse and subsequently into a leaky integrate–and–fire (LIF) neuron.
The input spikes are applied to the gates of the 1T1R structures as trains of voltage pulse with pulse lengths in the range of hundreds of nanoseconds. RRAM may be set into a high–conductance state (HCS) and reset into a low–conductance state (LCS) by providing an external positive voltage reference on Vtop and grounding Vbottom (LCS). The mean value of the HCS may be controlled by limiting the set programming (compliance) current (ICC) through the gate–source voltage of the series transistor. In the circuit, RRAMs perform two functions: They route and weigh input pulses.
Figure 1: The role of RRAM devices in neuromorphic circuits: (a) scanning electron microscopy (SEM) image of an HfO2 1T1R RRAM device, in blue, integrated on 130–nm CMOS technology, with its selector transistor (width of 650 nm) in green; (b) basic building block of the proposed neuromorphic circuit; (c) cumulative density function of the conductance of a population of 16–Kb RRAM devices, as a function of the compliance current ICC, which effectively controls the conductance level; (d) measurement of the circuit in (a); (e) measurement of the circuit in (b). (Source: “Neuromorphic object localization using resistive memories and ultrasonic transducers,” in Nature Communications)
“The op amp in Figure 1, along with transistors M1, M2, and M3, form the front–end circuit, which reads the current from the RRAM array and injects the current into the DPI synapse,” Vianello said. “The RRAM bottom electrode has a constant DC voltage Vbot applied to it, and the common top electrode is pinned to the voltage Vx by a rail–to–rail operational–amplifier circuit. The op–amp output is connected in negative feedback to its non–inverting input and has the constant DC bias voltage Vtop applied to its inverting input. As a result, the output of the op amp will modulate the gate voltage of transistor M1 such that the current it sources onto the node Vx will maintain its voltage as close as possible to the DC bias Vtop. Whenever an input pulse Vin arrives, a current equal to (Vx − Vbot)Gn will flow out of the bottom electrode. The negative feedback of the op amp will then act to ensure that Vx = Vtop by sourcing an equal current from transistor M1. By connecting the op–amp output to the gate of transistor M2, a current equal to it will therefore also be buffered into the branch composed of transistors M2 and M3 in series. This current is injected into a CMOS differential–pair integrator synapse circuit model, which generates an exponentially decaying waveform from the onset of the pulse with an amplitude proportional to the injected current.”
While traditional processing techniques sample the detected signal continuously and perform calculations to extract useful information, the proposed neuromorphic solution calculates asynchronously when useful information arrives, increasing the system’s energy efficiency by up to five orders of magnitude.
CEA–Leti has made significant developments in pMUT sensors and spiking neural networks based on RRAM technology during the last decade. “We would like to thank the H2020 MeM–Scales project [871371] that partially funded the work,” Vianello said.
Cyw
Regular
A long time ago, before online broking, I was in a broker's office, (Bridges if I remember correctly). I was buying some St George Bank shares. I looked at his computer at the quotes and asked what is all these things on the screen and he said it is how many shares people want yo buy or sell at what price. I asked him if there are so many people wanting to sell their shares, why should I buy it now? He said those who show their hands on the table are not the real big players. You will see the real players when lines got wiped from the board.
Now that everyone and his dog can see the quotes sitting at home or in the office or even on a bus, I think the large sell orders are there to encourage uninformed people to sell lower than the large order. If there are already 10 million shares offered at say, $1, you are not likely to join the end of the queue trying to sell your 5000 shares. Perhaps the large seller is trying to close his shorts at a lower price.
The other way around when the big boys want to short, they will put in large buy orders to encourage punters to buy their shorts at a higher price.
These days, I take the quotes on the table with a grain of salt. When the real players come in to buy, either the 750K will be pulled or wiped before you can say WTF.
David holland
Regular
Is this a good deal? How many chips go in a phone 1 ?
Makeme 2020
Regular
Dozzaman1977
Regular
Haha yes that is true. But as Renesas states in the diaper odour detector spec sheet....Diaper Odour Detector - WTF, not needed, the old human nose picks that shit up real quick (excuse the pun) and nappies are already attached to their own alarm system
System Benefits:
- Easily detects the odor from diapers and alerts caregivers in hospitals and/or nursing home facilities.
- The ZMOD4410 gas sensor module is designed for detecting total volatile organic compounds (TVOC).
Royalties!!!!
And I'm pretty sure I've heard brainchip talk about detecting volatile organic compounds in the past
If I was doing some quiet research into Smart Health, an area that Brainchips Akida can/does excel in, as mentioned by a number
of our company executives over the last few years, on how they expect big things for us from this sector, well, I have been made aware
that a paper on identifying bacteria in blood is due or expected to be due to be published within the next 3 weeks.
I believe that Brainchip is involved.......for the record, I don't speak on behalf of the company....just my own views at this point, thanks.
Regards........Tech x
of our company executives over the last few years, on how they expect big things for us from this sector, well, I have been made aware
that a paper on identifying bacteria in blood is due or expected to be due to be published within the next 3 weeks.
I believe that Brainchip is involved.......for the record, I don't speak on behalf of the company....just my own views at this point, thanks.
Regards........Tech x
uiux
Regular
I've been waiting for a follow up to this research:
pubs.rsc.org
High-speed particle detection and tracking in microfluidic devices using event-based sensing
Abstract
Visualising fluids and particles within channels is a key element of microfluidic work. Current imaging methods for particle image velocimetry often require expensive high-speed cameras with powerful illuminating sources, thus potentially limiting accessibility. This study explores for the first time the potential of an event-based camera for particle and fluid behaviour characterisation in a microfluidic system. Event-based cameras have the unique capacity to detect light intensity changes asynchronously and to record spatial and temporal information with low latency, low power and high dynamic range. Event-based cameras could consequently be relevant for detecting light intensity changes due to moving particles, chemical reactions or intake of fluorescent dyes by cells to mention a few. As a proof-of-principle, event-based sensing was tested in this work to detect 1 μm and 10 μm diameter particles flowing in a microfluidic channel for average fluid velocities of up to 1.54 m s−1. Importantly, experiments were performed by directly connecting the camera to a standard fluorescence microscope, only relying on the microscope arc lamp for illumination. We present a data processing strategy that allows particle detection and tracking in both bright-field and fluorescence imaging. Detection was achieved up to a fluid velocity of 1.54 m s−1 and tracking up to 0.4 m s−1 suggesting that event-based cameras could be a new paradigm shift in microscopic imaging.
High-speed particle detection and tracking in microfluidic devices using event-based sensing
Visualising fluids and particles within channels is a key element of microfluidic work. Current imaging methods for particle image velocimetry often require expensive high-speed cameras with powerful illuminating sources, thus potentially limiting accessibility. This study explores for the first...
High-speed particle detection and tracking in microfluidic devices using event-based sensing
Abstract
Visualising fluids and particles within channels is a key element of microfluidic work. Current imaging methods for particle image velocimetry often require expensive high-speed cameras with powerful illuminating sources, thus potentially limiting accessibility. This study explores for the first time the potential of an event-based camera for particle and fluid behaviour characterisation in a microfluidic system. Event-based cameras have the unique capacity to detect light intensity changes asynchronously and to record spatial and temporal information with low latency, low power and high dynamic range. Event-based cameras could consequently be relevant for detecting light intensity changes due to moving particles, chemical reactions or intake of fluorescent dyes by cells to mention a few. As a proof-of-principle, event-based sensing was tested in this work to detect 1 μm and 10 μm diameter particles flowing in a microfluidic channel for average fluid velocities of up to 1.54 m s−1. Importantly, experiments were performed by directly connecting the camera to a standard fluorescence microscope, only relying on the microscope arc lamp for illumination. We present a data processing strategy that allows particle detection and tracking in both bright-field and fluorescence imaging. Detection was achieved up to a fluid velocity of 1.54 m s−1 and tracking up to 0.4 m s−1 suggesting that event-based cameras could be a new paradigm shift in microscopic imaging.
DingoBorat
Slim
Shhhh! Think of the sales!Diaper Odour Detector - WTF, not needed, the old human nose picks that shit up real quick (excuse the pun) and nappies are already attached to their own alarm system
alwaysgreen
Top 20
Think of the shit fight this will cause with the shorters.Shhhh! Think of the sales!
I'd love to see the look on their faeces when this is announced as Akida.
They will all be downramping on the Crapper that day!
equanimous
Norse clairvoyant shapeshifter goddess
Japan loves Tech, Just look at their toilets ( I vouch for them). Smart Diapers will sellShhhh! Think of the sales!
It's not like you couldn't see it walking around Tokyo or the countryside. But one of the biggest indicators of this shift is that, with declining birthrates and over 25% of the population aged over 65, there are now more diapers produced for adults than for babies.”
VictorG
Member
The day will come when Akida will differentiate between a soiled diaper and Hot Crapper.Haha yes that is true. But as Renesas states in the diaper odour detector spec sheet....
System Benefits:
Imagine brainchip IP in each ZMOD4410 sensor in every diaper of the around the world in hospitals and nursing homes.....
- Easily detects the odor from diapers and alerts caregivers in hospitals and/or nursing home facilities.
- The ZMOD4410 gas sensor module is designed for detecting total volatile organic compounds (TVOC).
Royalties!!!!
And I'm pretty sure I've heard brainchip talk about detecting volatile organic compounds in the past
Boab
I wish I could paint like Vincent
Hi @uiuxI've been waiting for a follow up to this research:
High-speed particle detection and tracking in microfluidic devices using event-based sensing
Visualising fluids and particles within channels is a key element of microfluidic work. Current imaging methods for particle image velocimetry often require expensive high-speed cameras with powerful illuminating sources, thus potentially limiting accessibility. This study explores for the first...
High-speed particle detection and tracking in microfluidic devices using event-based sensing
Abstract
Visualising fluids and particles within channels is a key element of microfluidic work. Current imaging methods for particle image velocimetry often require expensive high-speed cameras with powerful illuminating sources, thus potentially limiting accessibility. This study explores for the first time the potential of an event-based camera for particle and fluid behaviour characterisation in a microfluidic system. Event-based cameras have the unique capacity to detect light intensity changes asynchronously and to record spatial and temporal information with low latency, low power and high dynamic range. Event-based cameras could consequently be relevant for detecting light intensity changes due to moving particles, chemical reactions or intake of fluorescent dyes by cells to mention a few. As a proof-of-principle, event-based sensing was tested in this work to detect 1 μm and 10 μm diameter particles flowing in a microfluidic channel for average fluid velocities of up to 1.54 m s−1. Importantly, experiments were performed by directly connecting the camera to a standard fluorescence microscope, only relying on the microscope arc lamp for illumination. We present a data processing strategy that allows particle detection and tracking in both bright-field and fluorescence imaging. Detection was achieved up to a fluid velocity of 1.54 m s−1 and tracking up to 0.4 m s−1 suggesting that event-based cameras could be a new paradigm shift in microscopic imaging.
do you think a Prophesee Brainchip combo could pull this off?
Cheers
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