Draft summary

paper.bib

@@ -1,3 +1,14 @@
+@article{davies2021advancing,
+  title={Advancing neuromorphic computing with loihi: A survey of results and outlook},
+  author={Davies, Mike and Wild, Andreas and Orchard, Garrick and Sandamirskaya, Yulia and Guerra, Gabriel A Fonseca and Joshi, Prasad and Plank, Philipp and Risbud, Sumedh R},
+  journal={Proceedings of the IEEE},
+  volume={109},
+  number={5},
+  pages={911--934},
+  year={2021},
+  publisher={IEEE}
+}
+
 @inproceedings{snyder2023neuromorphic,
 author = {Snyder, Shay and Risbud, Sumedh R. and Parsa, Maryam},
 title = {Neuromorphic Bayesian Optimization in Lava},

paper.md

@@ -45,14 +45,17 @@ bibliography: paper.bib
 
 # Summary
 
-- Challenges of optimization and opportunities of neuromorphic computing
-  - Scalability, low latency, optimality, energy
-  - Neuromorphic computing provides fine-grained parallel and event-driven copmutation, low energy consumption
-- Development cycle is typically long for neuromorphic computing due to the lack of effective abstraction frameworks
+Solving real-world mathematical optimization problems requires modern solvers to meet increasignly strict requirements, such as low latency, high solution quality, low energy consumption, and support for massive scalability. Neuromorphic computing is emerging as a promising paradygm for fine-grained parallel and event-driven computation, enabling orders of magnitude gains in Energy-Delay-Product on optimization workloads [@davies2021advancing]. 
+However, neuromorphic applications tipically suffers from a long development cycle, since the lack of effective abstraction frameworks requires deep knowledge of the target hardware platform and limits contributions from domain experts (e.g., operations researcher).
+`Lava Optimization` is a Python package ... algorithms and applications in the area of mathematical optimization.
+
+The library provides 
+
+We leveraged this software infrastructure to develop solvers for continuous Quadratic Programming (QP) and Quadratic Unconstrained Binary Optimization (QUBO) problems, while the community contributed a Bayesian solver [@snyder2023neuromorphic] and the Local Competitive Algorithm (LCA) [@parpart2023implementing].
+
 - `Lava Optimization` increases productivity on developing and testing novel neuromorphic algorithms and applications
 - The library abstracts away the neuromoprhic aspect of the backend, exposing an API typical of constrained optimization (variables, constraints, cost, etc.)
 - Supports the community  in developing algorithms that are iterative, discrete, and distributed
-- We leveraged the library architecture to develop multi-backend QUBO and QP solvers, and received contirbutions from the community for a Bayesian [@snyder2023neuromorphic] and LCA [@parpart2023implementing] solvers
 
 # Statement of need