feat: Add contribution CTA to pages

This commit adds contribution CTAs to the hardware and computing pages to encourage community involvement.

Author: neural-loop

content/neuromorphic-computing/student-talks/legendre-snn-on-loihi-2/index.md

@@ -17,29 +17,10 @@ for Time Series Classification (TSC). Reservoir Computing is a well-established
 processing where a reservoir of statically (and recurrently) connected neurons compute high
 dimensional temporal features, over which a linear readout layer learns the mapping to the output.'
 ---
-
-In his recent work [1], Ram designed the Legendre-SNN (LSNN), a simple - yet high performing SNN model
-(for univariate TSC) where he has used the Legendre Delay Network (LDN) [2] as a non-spiking reservoir
-(in fact, the LDN in LSNN is implemented with just basic matrix-operations). In a subsequent work
-(currently under review), he extended his LSNN to DeepLSNN that accounts for multivariate time-series
-signals too; upon experimenting with it, he found that DeepLSNN models outperform a popular (and
-complex) LSTM-Conv integrated model [3] on more than 30% of 101 TSC datasets. His latest work is on
-the evaluation of Legendre-SNN on the Loihi-2 chip [4] — on which this talk is focused at.
-Legendre-SNN is composed of a non-spiking LDN followed by one spiking hidden layer and an output
-layer. The Loihi-2 chip has got two On-chip computational resources: low-power x86 Lakemont (LMT)
-microprocessors (total 6) and NeuroCores (total 128). The LMT cores support only INT32-bit operations
-and NeuroCores support deployment of spiking networks. With minimal documentation to program the
-LMT cores, the challenge was -- how to deploy the Legendre-SNN in its entirety (and evaluate it) on a
-Loihi-2 chip. In this talk, Ram will present the technical specifics of implementing the non-spiking LDN on
-an LMT core (the spiking network post the LDN is deployed on NeuroCores). His work: “Legendre-SNN
-on Loihi-2” [4] adds to the scarce technical-documentation to program LMT cores and presents a pipeline
-to deploy an SNN model composed of non-spiking & spiking components -- entirely on Loihi-2.
-
+In his recent work [1], Ram designed the Legendre-SNN (LSNN), a simple - yet high performing SNN model (for univariate TSC) where he has used the Legendre Delay Network (LDN) [2] as a non-spiking reservoir (in fact, the LDN in LSNN is implemented with just basic matrix-operations). In a subsequent work (currently under review), he extended his LSNN to DeepLSNN that accounts for multivariate time-series signals too; upon experimenting with it, he found that DeepLSNN models outperform a popular (and complex) LSTM-Conv integrated model [3] on more than 30% of 101 TSC datasets. His latest work is on the evaluation of Legendre-SNN on the Loihi-2 chip [4] — on which this talk is focused at.
+Legendre-SNN is composed of a non-spiking LDN followed by one spiking hidden layer and an output layer. The Loihi-2 chip has got two On-chip computational resources: low-power x86 Lakemont (LMT) microprocessors (total 6) and NeuroCores (total 128). The LMT cores support only INT32-bit operations and NeuroCores support deployment of spiking networks. With minimal documentation to program the LMT cores, the challenge was -- how to deploy the Legendre-SNN in its entirety (and evaluate it) on a Loihi-2 chip. In this talk, Ram will present the technical specifics of implementing the non-spiking LDN on an LMT core (the spiking network post the LDN is deployed on NeuroCores). His work: “Legendre-SNN on Loihi-2” [4] adds to the scarce technical-documentation to program LMT cores and presents a pipeline to deploy an SNN model composed of non-spiking & spiking components -- entirely on Loihi-2.
 References:
-[1]: Gaurav, Ramashish, Terrence C. Stewart, and Yang Yi. "Reservoir based spiking models for univariate Time Series
-Classification." Frontiers in Computational Neuroscience 17 (2023): 1148284.
-[2]: Voelker, Aaron R., and Chris Eliasmith. "Improving spiking dynamical networks: Accurate delays, higher-order synapses,
-and time cells." Neural computation 30.3 (2018): 569-609.
+[1]: Gaurav, Ramashish, Terrence C. Stewart, and Yang Yi. "Reservoir based spiking models for univariate Time Series Classification." Frontiers in Computational Neuroscience 17 (2023): 1148284.
+[2]: Voelker, Aaron R., and Chris Eliasmith. "Improving spiking dynamical networks: Accurate delays, higher-order synapses, and time cells." Neural computation 30.3 (2018): 569-609.
 [3]: Karim, Fazle, et al. "LSTM fully convolutional networks for time series classification." IEEE access 6 (2017): 1662-1669.
-[4]: Gaurav, Ramashish, Terrence C. Stewart, and Yang Yi. "Legendre-SNN on Loihi-2: Evaluation and Insights." NeurIPS 2024
-Workshop Machine Learning with new Compute Paradigms.
+[4]: Gaurav, Ramashish, Terrence C. Stewart, and Yang Yi. "Legendre-SNN on Loihi-2: Evaluation and Insights." NeurIPS 2024 Workshop Machine Learning with new Compute Paradigms.

layouts/neuromorphic-computing/list.html

@@ -1,8 +1,19 @@
 {{ define "main" }}
 {{ partial "page-header" . }}
 
+
 <section class="section pt-6 mt-0 pb-16">
   <div class="container">
+
+    <div>
+      {{ partial "components/content-contribute-cta.html" (dict
+      "icon" "solid brain"
+      "title" "Join the <span class=\"gradient-text\">Conversation</span>"
+      "description" "The Open Neuromorphic community thrives on collaboration. Join our Discord, attend an event, or contribute to a project to help push the boundaries of brain-inspired computing."
+      "link" "/getting-involved/"
+      "link_text" "Find Your Place in Our Community"
+      ) }}
+    </div>
     {{/* Display the main content of the _index.md page */}}
     <div class="content text-center mb-12 prose dark:prose-invert max-w-none">
         {{ .Content }}
@@ -83,7 +94,7 @@
         "page_context" .
       )
     }}
-    
+
     <!-- Recent Posts -->
     <div class="mb-0">
       <h3 class="section-title text-3xl font-semibold mb-3">Blog</h3>
@@ -101,17 +112,6 @@ <h3 class="section-title text-3xl font-semibold mb-3">Blog</h3>
          <a href="{{ "blog/" | relLangURL }}" class="btn btn-new-primary">View All Posts</a>
       </div>
     </div>
-    
-    <div class="mt-16">
-      {{ partial "components/content-contribute-cta.html" (dict
-        "icon" "solid brain"
-        "title" "Join the <span class=\"gradient-text\">Conversation</span>"
-        "description" "The Open Neuromorphic community thrives on collaboration. Join our Discord, attend an event, or contribute to a project to help push the boundaries of brain-inspired computing."
-        "link" "/getting-involved/"
-        "link_text" "Find Your Place in Our Community"
-      ) }}
-    </div>
-
   </div>
 </section>
 {{ end }}

layouts/neuromorphic-hardware/single.html

@@ -22,7 +22,17 @@
           <div class="content mb-10">
             {{ .Content }}
           </div>
+          <div class="max-w-4xl mx-auto mt-12 mb-16">
+            {{ partial "components/content-contribute-cta.html" (dict
+            "icon" "solid microchip"
+            "title" "Help Us <span class=\"gradient-text\">Improve this Guide</span>"
+            "description" "Our hardware guide is community-maintained. If you know of a chip we should add, see an error, or have updated information, please let us know by opening an issue on our GitHub repository."
+            "link" "https://github.com/open-neuromorphic/open-neuromorphic.github.io/issues/new/choose"
+            "link_text" "Suggest an Edit on GitHub"
+            ) }}
+          </div>
           {{ partial "components/share-cta.html" . }}
+
         </article>
         {{ partial "components/sidebar-toc-shared.html" . }}
       </div>