<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Hardware on Fabrizio Ottati</title><link>https://fabrizio-ottati.github.io/tags/hardware/</link><description>Recent content in Hardware on Fabrizio Ottati</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Tue, 26 Dec 2023 00:00:00 +0000</lastBuildDate><atom:link href="https://fabrizio-ottati.github.io/tags/hardware/index.xml" rel="self" type="application/rss+xml"/><item><title>Neural inference at the frontier of energy, space and time - NorthPole, IBM</title><link>https://fabrizio-ottati.github.io/blog/northpole/</link><pubDate>Tue, 26 Dec 2023 00:00:00 +0000</pubDate><guid>https://fabrizio-ottati.github.io/blog/northpole/</guid><description>&lt;figure&gt;&lt;img src="https://fabrizio-ottati.github.io/blog/northpole/on-chip-cartoon.png" width="600px"&gt;&#10;&lt;/figure&gt;&#10;&#10;&lt;p&gt;&lt;a href="https://research.ibm.com/blog/northpole-ibm-ai-chip"&gt;NorthPole&lt;/a&gt; is the new&#10;shiny artificial intelligence (AI) accelerator developed by IBM.&lt;/p&gt;&#10;&lt;blockquote&gt;&#10;&lt;p&gt;NorthPole, an architecture and a programming model for neural inference,&#10;reimagines (Fig. 1) the interaction between compute and memory by embodying 10&#10;interrelated, synergistic axioms that build on brain-inspired computing.&lt;/p&gt;&#10;&lt;/blockquote&gt;&#10;&lt;p&gt;The creators claim that NorthPole design has been driven by 10 axioms inspired&#10;by human biology. We will use them as guidelines to analyze the paper, keeping&#10;the same outline as the original article.&lt;/p&gt;</description></item><item><title>Neural inference at the frontier of energy, space and time - NorthPole, IBM</title><link>https://fabrizio-ottati.github.io/draft/northpole/</link><pubDate>Sat, 25 Nov 2023 00:00:00 +0000</pubDate><guid>https://fabrizio-ottati.github.io/draft/northpole/</guid><description>&lt;p&gt;&lt;a href="https://research.ibm.com/blog/northpole-ibm-ai-chip"&gt;NorthPole&lt;/a&gt; is the new&#10;shiny artificial intelligence (AI) accelerator developed by IBM. The creators&#10;claim that NorthPole design has been driven by 10 axioms inspired by human&#10;biology. We will use them as guidelines to analyze the paper.&lt;/p&gt;&#10;&lt;p&gt;The outline of this blog post is the same as the original article.&lt;/p&gt;&#10;&lt;h1 id="axiomatic-design"&gt;Axiomatic design&lt;/h1&gt;&#10;&lt;blockquote&gt;&#10;&lt;p&gt;NorthPole, an architecture and a programming model for neural inference,&#10;reimagines (Fig. 1) the interaction between compute and memory by embodying 10&#10;interrelated, synergistic axioms that build on brain-inspired computing.&lt;/p&gt;&#10;&lt;/blockquote&gt;</description></item><item><title>Installing Vitis HLS and Vivado on Fedora 37</title><link>https://fabrizio-ottati.github.io/blog/install-vitis-on-fedora-37/</link><pubDate>Thu, 25 May 2023 00:00:00 +0000</pubDate><guid>https://fabrizio-ottati.github.io/blog/install-vitis-on-fedora-37/</guid><description>&lt;p&gt;Fedora 37 is not officially supported by Xilinx (or AMD, I should say). Hence, I had to use some workarounds to install it on my work machine.&lt;/p&gt;&#10;&lt;p&gt;These instructions have been tested for the 2022.2 and 2023.1 versions.&lt;/p&gt;&#10;&lt;h1 id="why-vitisvivado"&gt;Why Vitis/Vivado?&lt;/h1&gt;&#10;&lt;p&gt;Vitis HLS and Vivado are &lt;strong&gt;freeware&lt;/strong&gt;! This means that the tool itself is not open source, but anyone can download it and use it! I use it in my research and I find it useful to be able to run some small testbenches on my local machine without ssh-ing to the lab server.&lt;/p&gt;</description></item><item><title>Bits of Chips | TrueNorth</title><link>https://fabrizio-ottati.github.io/blog/truenorth/</link><pubDate>Sun, 26 Mar 2023 00:00:00 +0000</pubDate><guid>https://fabrizio-ottati.github.io/blog/truenorth/</guid><description>&lt;p&gt;&lt;img src="brain-to-chip.png" alt="The beautiful TrueNorth article image"&gt;&lt;/p&gt;&#10;&lt;h2 id="why-do-we-want-to-emulate-the-brain"&gt;Why do we want to emulate the brain?&lt;/h2&gt;&#10;&lt;p&gt;If you have ever read an article on neuromorphic computing, you might have noticed that in the introduction of each of these there is the same statement: &amp;ldquo;The brain is much powerful than any AI machine when it comes to cognitive tasks but it runs on a &lt;strong&gt;10W&lt;/strong&gt; power budget!&amp;rdquo;. This is absolutely true: neurons in the brain communicate among each other by means of &lt;strong&gt;spikes&lt;/strong&gt;, which are short voltage pulses that propagate from one neuron to the other. The average spiking activity is estimated to be around &lt;strong&gt;10Hz&lt;/strong&gt; (i.e. a spike every 100ms). This yields &lt;strong&gt;very low processing power consumption&lt;/strong&gt;, since the activity in the brain results to be &lt;strong&gt;really sparse&lt;/strong&gt; (at least, this is the hypothesis).&lt;/p&gt;</description></item><item><title>Spiking neurons: a digital hardware implementation</title><link>https://fabrizio-ottati.github.io/blog/intro-digital-hw-snn/</link><pubDate>Tue, 03 Jan 2023 00:00:00 +0000</pubDate><guid>https://fabrizio-ottati.github.io/blog/intro-digital-hw-snn/</guid><description>&lt;p&gt;In this article, we will try to model a layer of Leaky Integrate and Fire (LIF) spiking neurons using digital hardware: registers, memories, adders and so on. To do so, we will consider a single output neuron connected to multiple input neurons from a previous layer.&lt;/p&gt;&#10;&lt;p&gt;&lt;img src="https://fabrizio-ottati.github.io/images/blog/intro-digital-hw-snn/neurons-connected.png" alt="Multiple pre-synaptic neurons connected to a post-synaptic one."&gt;&lt;/p&gt;&#10;&lt;p&gt;In a Spiking Neural Network (SNN), neurons communicate by means of &lt;strong&gt;spikes&lt;/strong&gt;: these activation voltages are then converted to currents through the &lt;strong&gt;synapses&lt;/strong&gt;, charging the &lt;strong&gt;membrane potential&lt;/strong&gt; of the destination neuron. In the following, the destination neuron is denoted as &lt;strong&gt;post-synaptic&lt;/strong&gt; neuron, with the index $i$, while the input neuron under consideration is denoted as &lt;strong&gt;pre-synaptic&lt;/strong&gt; neuron, with the index $j$.&lt;/p&gt;</description></item></channel></rss>