Two recent articles indicate a certain pick up of Domain-specific Accelerators adoption. With the end of Moore's Law, domain-specific hardware solution remains one of the few paths to continuing to increase the performance and efficiency of computing hardware.
For a long time, domain-specific Accelerators adoption was limited by economics factors. Historically, the small feature sizes, small batch sizes, and high cost of fab time (for ASICs) translated in a prohibitive per unit cost. However, economic factors have shifted :
move toward standardised opensource tooling,
more flexible licensing model,
RISC-V architecture coming of age and maturing rapidly
Fab cost dropping
Wide availability of FPGA (AWS F1)
Rise of co-designed high-level programming language reducing the learning curve and design cycle.
power/performance wall of general-purpose compute unit
We are about to see a dramatic shift toward heterogeneous compute infrastructure over the next couples of years.
A Survey on Homomorphic Encryption Schemes : this paper presents a thorough survey of the state of homomorphic encryption schemes. Homomorphic encryption allows manipulation of the encrypted data without the need to decrypt it. This will allow when hardware will be fast enough to deal with the complexity of the operations, to have a true secure distributed multitenant database. As no operation on the hosting side will require clear text decryption of the data and everything can be done securely on the client side.
Efficient Methods and Hardware for Deep Learning : Standford lecture where guest lecturer Song Han present algorithms and specialized hardware (FPGA, GPU, ASIC, etc..) that can be used to accelerate training and inference of deep learning workloads. [video]
