Everyone knows that arithmetic is true: 2 + 2 = 4.But surprisingly, we don't know why it's true.

Monotonicity is the intuition of "order preserving" and helps us keep track of our place in the world, so that when we approach an object it looms larger but smaller when we move away.

Convexity is grounded in intuitions of "betweenness". For example, the four corners of a football pitch define the playing field even without boundary lines connecting them.

Continuity describes the smoothness with which objects seem to move in space and time.

Isomorphism is the idea of sameness or analogy. It's what allows us to recognise that a cat is more similar to a dog than to a rock.

“This is going to represent one of the three major pillars of computing going forward,” NVIDIA CEO Jensen Huang said

“The CPU is for general-purpose computing, the GPU is for accelerated computing, and the DPU, which moves data around the data center, does data processing.”

What's a DPU? System on a chip that combines: Industry-standard, high-performance, software-programmable multi-core CPU High-performance network interface Flexible and programmable acceleration engines

CPU v GPU v DPU: What Makes a DPU Different?  A DPU is a new class of programmable processor that combines three key elements. A DPU is a system on a chip, or SoC, that combines: An industry-standard, high-performance, software-programmable, multi-core CPU, typically based on the widely used Arm architecture, tightly coupled to the other SoC components. A high-performance network interface capable of parsing, processing and efficiently transferring data at line rate, or the speed of the rest of the network, to GPUs and CPUs. A rich set of flexible and programmable acceleration engines that offload and improve applications performance for AI and machine learning, zero-trust security, telecommunications and storage, among others. All these DPU capabilities are critical to enable an isolated, bare-metal, cloud-native computing platform that will define the next generation of cloud-scale computing.

At a minimum, there 10 capabilities the network data path acceleration engines need to be able to deliver: Data packet parsing, matching and manipulation to implement an open virtual switch (OVS) RDMA data transport acceleration for Zero Touch RoCE GPUDirect accelerators to bypass the CPU and feed networked data directly to GPUs (both from storage and from other GPUs) TCP acceleration including RSS, LRO, checksum, etc. Network virtualization for VXLAN and Geneve overlays and VTEP offload Traffic shaping “packet pacing” accelerator to enable multimedia streaming, content distribution networks and the new 4K/8K Video over IP (RiverMax for ST 2110) Precision timing accelerators for telco cloud RAN such as 5T for 5G capabilities Crypto acceleration for IPSEC and TLS performed inline, so all other accelerations are still operational Virtualization support for SR-IOV, VirtIO and para-virtualization Secure Isolation: root of trust, secure boot, secure firmware upgrades, and authenticated containers and application lifecycle management

Key vendors in the DPU market include NVIDIA, Marvell, Fungible (acquired by Microsoft), Broadcom, Intel, Resnics, and AMD Pensando.