If you’re at CVPR this week, stop by one of our flash sessions to learn how NVIDIA NIM accelerates model deployment.
Lambda’s Post
More Relevant Posts
-
𝗥𝗲𝗮𝗱𝗶𝗻𝗴 𝗗𝗮𝘁𝗮𝘀𝗵𝗲𝗲𝘁𝘀 - 𝗧𝗼𝗴𝗴𝗹𝗶𝗻𝗴 𝗚𝗣𝗜𝗢: https://lnkd.in/gZ5kqd95 Using the example of how to toggle a GPIO pin, we demonstrate how to go about reading the data-sheet/manual for a controller and make sense of it. 𝗧𝗮𝗯𝗹𝗲 𝗼𝗳 𝗰𝗼𝗻𝘁𝗲𝗻𝘁𝘀 00:00 Topic for today: Navigating the Datasheet to toggle the GPIO 00:39 Significance of the activity 02:19 Device Family 04:00 How to find the datasheet/manual 05:02 What information does the datasheet/manual have? 06:05 Device type, system architecture and processor 07:22 Boot up - VTOR and Stackpointer 08:27 ARM-M boot flow 09:54 Boot address and VTOR 11:29 Boot configuration 13:53 Memory map and finding memory addresses 15:25 Mapping of the memories 17:02 Clock Configuration -Reset and Clock control 20:16 Clock Source: Internal and External Oscillators 22:01 Clocking the GPIO block 25:06 Enabling the Clocks by writing to registers 27:33 Base Address and Offsets 29:22 How do we know which clock goes to GPIO block? 32:21 Toggling the GPIO - Configuring the PIN 38:17 A quick revision: how we turned ON/OFF the GPIO 40:07 How the GPIO example is a universal one 40:58 Putting it all together! 44:41 Tech Glitch and End ----- 🚨I noticed that my posts don't reach all my connections/followers due to the LinkedIn algorithm. To not miss the buy link to the book - 𝗖 𝗡𝗶𝗻𝗷𝗮, 𝗶𝗻 𝗣𝘆𝗷𝗮𝗺𝗮! be on the mailing list here: https://lnkd.in/gUkWjdwA 👉 For more Embedded Systems related posts, follow Piyush Itankar 🙌 #inpyjama #EmbeddedSystemsPodcast #podcastInPyjama #embeddedsystems
5. Reading Datasheets - Toggling GPIO [Embedded systems podcast, in Pyjama]
https://www.youtube.com/
To view or add a comment, sign in
-
How are you transitioning your companies to the next frontier
All you need to know about Nvidia Blackwell in 5 mins.
To view or add a comment, sign in
-
Founder - DamageBDD - Behavior Verification At Planetary Scale. ⩓ ₿ 🗲 Æ. zap me npub1zmg3gvpasgp3zkgceg62yg8fyhqz9sy3dqt45kkwt60nkctyp9rs9wyppc
How many transistors would it require to replicate the human brain? [mistral 7b, local rtx3090] Estimating the number of transistors required to replicate the human brain involves comparing the computational complexity and storage capacity of the brain to that of modern transistors. The human brain contains approximately 86 billion neurons, with each neuron forming about 1,000 to 10,000 synapses with other neurons, leading to a total of roughly 100 trillion synapses. To simplify, if we consider each synapse to be analogous to a transistor (a vast oversimplification, as synapses are far more complex and capable of modulation and plasticity), we could start with a figure of 100 trillion transistors. However, to truly replicate the functionality of the human brain, we'd need to account for the dynamic, analog nature of synapses, their capability for learning (synaptic plasticity), and the parallel processing capabilities of the brain. This would require a significantly more complex and capable system than one where each synapse is represented by a single transistor. Furthermore, the efficiency of the brain far surpasses current technology; it operates on about 20 watts of power, a feat that current computers, even with billions of transistors, cannot match for the same type of computation and energy efficiency. Advancements in neuromorphic computing, which aims to mimic neural architectures in hardware, suggest that simply counting transistors is not enough to capture the full complexity of brain-like computation. Neuromorphic chips, like IBM's TrueNorth, use transistors in a way that more closely resembles the operations of neurons and synapses, focusing on pattern recognition and sensory processing with high efficiency. In summary, while a rough estimate might start at 100 trillion transistors to match the number of synapses, the true number required to replicate the full functionality and efficiency of the human brain would likely be much higher and would depend on significant advancements in how we design and utilize those transistors. #humanityfirst #humanchampions #apex #evolutionaryoptimum
All you need to know about Nvidia Blackwell in 5 mins.
To view or add a comment, sign in
-
Transformations are a foot, so significant. All of us may be missing it in super computing with the new release of Nvidia’s new Blackwell chipset. Billions of computations and terra bites of data analyzed in a second. The scale is massive and will be disruptive. There new chip set will expand the capability of generative AI on a level that will create a revolution in software development. DGX SuperPOD is built with NVIDIA DGX™ GB200 systems and provides 11.5 exaflops of AI supercomputing at FP4 precision and 240 terabytes that is there next version. Supposedly there a few exaflop capable sources today. In tomorrows world you can get that in one rack. That’s trillions of transistors and terabytes of data all talking together at once. If you do anything, watch this 5 minute video clip. Just to get a sense.
All you need to know about Nvidia Blackwell in 5 mins.
To view or add a comment, sign in
-
Great high bandwidth chiplet design
All you need to know about Nvidia Blackwell in 5 mins.
To view or add a comment, sign in
-
Brain v/s chip ——————— Meet #blackwell, the successor to #hopper that “changed the world” 208 billion transistors. By comparison, first Intel chip for personal computers was the Intel 4004, introduced in 1971. It was a 4-bit central processing unit (CPU). The Intel 4004 contained 2,300 transistors. It’s estimated 100 Trillion transistors are needed to replicate the human brain 🧠 The human brain is however immensely complex, with around 86 billion neurons and an estimated 100 trillion synapses. Each neuron in the brain is connected to thousands of other neurons through synapses, forming intricate networks that enable complex cognitive functions. Replicating this level of connectivity and functionality goes beyond just the number of transistors in a chip. However for many specific functions, the brain will no longer be needed. However the brain works on 20watts and costs little to make one ❤️ #nvidia
All you need to know about Nvidia Blackwell in 5 mins.
To view or add a comment, sign in
-
All you need to know about Nvidia Blackwell in 5 mins.
All you need to know about Nvidia Blackwell in 5 mins.
To view or add a comment, sign in