Ann: BrainChip to present at TechKnow Invest Roadshow, page-15

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Hiren Majmudar Linkedin

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Vice President & Managing Director, Intel Capital

Austin, Texas

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  Compute in memory with both DRAM and SRAM approaches are feasible and being researched by promising folks. Obviously one is difficult to manufacture/scale w.r.t. computing vs. other given current manufacturing process of choice.
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  With barrage of announcements coming out of AI HW Summit last week, here are my thoughts on where the compute innovations are progressing. Silicon innovation, valuations, VC$ for Silicon are all at an all time high. #CPU #GPU #FPGA #machinelearning #chiparchitecture 1. CPUs will support more inference because they still have headroom of free cycles; GPUs, with added power, will also support more inference. CPUs and GPUs are here to stay (ease of programming and general purpose)! 2. FPGAs are flexible but industry has to solve the cost & ease of programming issues. Innovation towards ease of programming for FPGA will likely have outsized returns. 3. ASICs offer an order of magnitude $$ & Energy efficiency but are less flexible than FPGA. ASICs allow more compute parallelism, ability to optimize memory bandwidth and hence better for emerging “purpose driven architectures”. There will be few winners in this race. 4. Compute in memory is about to become real, will improve latency significantly; Photonic computing may be the next frontier of innovation. 5. Too many open source FWs (for NN models) are painful for HW providers. Standardization is needed. Starting to see early signs of this. 6. Too many benchmarks for showing the value of silicon for AI.
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  Congratulations DavidD & Avigdor
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  Inference on Edge innovation continues. Spiking Neural Network SNNs with a type of neuromorphic architecture from Brainchip.
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