Achieving near-perfect symmetry with Nighthawk architecture

Topoconductor enabling a hardware architecture for quantum computing that the company says could push us from decades to years in achieving scalable quantum systems

Never thought I'd say this, but the best edge Open Model might just belong to IBM. When compared to Qwen, LFM and Gemma on a series of General Knowledge, Math, Code and Safety benchmarks, Granite 4.0 Nano models demonstrated a significant increase in capabilities that can be achieved with a minimal parameter footprint. These new 1B and 350M parameter models are available with hybrid SSM-based architecture, or as traditional transformer versions so that they can be used where hybrid architectures may not yet have optimized support. Very interesting to see that Hybrid does better with the small model, but standard is better with big (although it does have more params). If I had to infer a trend, I'd say that we're still figuring out scaling tricks for the newer architectures, although data is very limited to make this claim. Will report back with my experiments and results. Stay tuned.

topoconductor, enabling a hardware architecture for quantum computing that the company says could push us from decades to years in achieving scalable quantum systems

Architecture Selection Decision Framework: A systematic flowchart for choosing neural network architectures based on data characteristics and deployment constraints. The process begins with data type identification (text/sequences/images/tabular) to select initial architecture candidates (Transformers/RNNs/CNNs/MLPs), then iteratively evaluates memory budget, computational cost, inference speed, accuracy targets, and hardware compatibility.

Arm’s Confidential Compute Architecture isnt exactly novel. Way back in the 70s the Mutlics project used 8 rings protected by gates to isolate memory. Multics still assumed a trusted monitor with lattice style information controls where CCA assumes the kernel/hypervisor can’t be trusted.

🚀 Day 7: SystemVerilog series Topic ---- queues in sv In SystemVerilog, queues are one of the most powerful and flexible types of arrays. Unlike static or dynamic arrays, queues can grow and shrink automatically, making them perfect for handling variable-sized data structures like FIFOs or buffers. . 🧠 What is a Queue? A queue is a variable-size, ordered collection of elements that allows insertion and deletion from both ends. ✅ Automatically grows or shrinks as elements are added or removed. ✅ Indexing starts from 0. ✅ Can use built-in methods to manipulate data. 📝 Why Use Queues in SV? Efficient for variable data handling. Simplifies FIFO/LIFO implementation. Easy insertion/deletion without manual shifting. 💡 Pro Tips: ✅ Use queues when data size is unpredictable. → Unlike dynamic arrays, queues automatically grow and shrink — no need for manual new[]. 🧮 Always check size before popping: if(q.size() > 0) q.pop_front(); → Prevents simulation errors from empty queue access. #SystemVerilog #Verification #VLSI #DesignVerification #LearningSeries #UVM #Queue #SV

real RTOS is the only real RTOS All others will: 1. Execute so much context switching logic in the application's ISR that it becomes 4 times longer. 2. Disable the application's interrupts for a time that grows with the number of tasks. 3. In a multi-core system, allow lower priority ISRs and tasks executing on separate cores to impeded the highest priority ISR. All these deficiencies can cause loss of the highest priority data. For applications upon which human life and significant assets can depend, this is not acceptable. Only real RTOS with its patented Defer Structure Architecture corrects these deficiencies. https://lnkd.in/gthnuN2K https://lnkd.in/gCue6CH5 #rtos #realtime #embedded

Latency in System Design — Explained in 60 Seconds! Latency is one of the most critical — and often misunderstood — metrics in system design. It’s not just about “speed,” but how quickly a system responds to a single request. In this short video, I explain: 🔹 What latency means in distributed systems 🔹 How it affects scalability and user experience 🔹 Why even a few milliseconds matter in large-scale architectures If you’re preparing for system design interviews or building real-world scalable systems, understanding latency is essential. 📺 Watch the short video here 👉 https://lnkd.in/dX_mdJV2 💬 What’s the lowest latency system you’ve ever worked on? Share your experience! #SystemDesign #Latency #Scalability #BackendEngineering #DistributedSystems #SoftwareArchitecture #TechExplained #EngineeringInsights

"For eight decades, rigid hardware designs have forced software to adapt, with modern processors dedicating roughly 98% of silicon to overhead and just 2% to actual computation,” said Elad Raz, NextSilicon’s Founder and CEO. “Maverick-2 flips this paradigm on its head by devoting the majority of hardware real estate to compute, moving runtime overhead management to intelligent algorithms and software in real-time through our dataflow architecture.” #NextSilicon #Maverick2 #HighPerformanceComputing #ArtificialIntelligence #DataflowArchitecture #Innovation #TechLeadership https://lnkd.in/d4QAaV8p

Getting close to releasing the first product using the Reactive Compute solution for Agentic Processing with the Reactive Compute Cores. Think of micro-service and open protocol agenting systems. This provides the background of the Reactive Compute mission statement to disrupt the established n-tier distributed micro-services architecture with dynamic minimal micro-service workers and open-protocol agentic processing.