Computer Engineering podcasts

Gaute Einevoll and Mihai Petrovici discuss how neuromorphic hardware can support artificial intelligence and efficient brain simulation. They contrast high-frequency arithmetic on conventional silicon with the rich dynamics and millisecond spikes of biological neurons, and explore brain-inspired systems for emulating neural dynamics and studying computation in spiking networks. Petrovici is a senior researcher at the University of Bern. The conversation was recorded on 18 June 2026 and released on 23 September 2026; the publisher’s audio runs 1 hour, 41 minutes and 46 seconds.

Hosted by Gaute Einevoll

Hamish Johnston interviews Yonatan Cohen, co-founder and chief technology officer of Quantum Machines, about control systems linking quantum computers to classical computation. Topics include the increasing importance of hybrid control as qubit counts grow, support for superconducting circuits, trapped ions, neutral atoms, spins and nitrogen-vacancy centers, industry collaborations and careers in quantum technology.

Hosted by Hamish Johnston

Fireworks AI co-founder Benny Chen explains the infrastructure required to serve machine-learning models at scale, from GPU procurement and kernels to runtimes and request routing. He discusses power constraints, hardware utilization, mixture-of-experts models, quantization, reinforcement-learning efficiency and speculative decoding. The conversation also covers distributed training across data centres, sparse autoencoders, the constraints on open-source interpretability research, and the relationship between open models and compute availability. Hosted by Ravid Shwartz Ziv and Allen Roush; the full conversation is available on YouTube.

Hosted by Ravid Shwartz Ziv, Allen Roush

Zain Asgar examines running agent inference across heterogeneous hardware. The discussion covers splitting workloads between GPUs and CPUs, compilation for different targets, automated kernel optimisation and hardware-aware scheduling. It also explores networking constraints and trade-offs among numerical precision, application accuracy, performance and cost.

Hosted by Sam Charrington

Kunle Olukotun explains reconfigurable dataflow architectures for AI inference. The discussion examines mapping computation onto model dataflow graphs, reducing memory-bandwidth bottlenecks, serving multiple models and switching between them efficiently. It also considers dynamic hardware architectures and using AI agents to develop compilers.

Hosted by Sam Charrington

Ralph Bernstein discusses semiconductor chips, miniaturized transistors and the specialized manufacturing behind modern computing. Conversation in Norwegian.